src/HOL/Analysis/Convex.thy

clarified sessions;

(* Title:      HOL/Analysis/Convex.thy
   Author:     L C Paulson, University of Cambridge
   Author:     Robert Himmelmann, TU Muenchen
   Author:     Bogdan Grechuk, University of Edinburgh
   Author:     Armin Heller, TU Muenchen
   Author:     Johannes Hoelzl, TU Muenchen
*)

section \<open>Convex Sets and Functions\<close>

theory Convex
imports
  Affine
  "HOL-Library.Set_Algebras"
begin

subsection \<open>Convex Sets\<close>

definition\<^marker>\<open>tag important\<close> convex :: "'a::real_vector set \<Rightarrow> bool"
  where "convex s \<longleftrightarrow> (\<forall>x\<in>s. \<forall>y\<in>s. \<forall>u\<ge>0. \<forall>v\<ge>0. u + v = 1 \<longrightarrow> u *\<^sub>R x + v *\<^sub>R y \<in> s)"

lemma convexI:
  assumes "\<And>x y u v. x \<in> s \<Longrightarrow> y \<in> s \<Longrightarrow> 0 \<le> u \<Longrightarrow> 0 \<le> v \<Longrightarrow> u + v = 1 \<Longrightarrow> u *\<^sub>R x + v *\<^sub>R y \<in> s"
  shows "convex s"
  using assms unfolding convex_def by fast

lemma convexD:
  assumes "convex s" and "x \<in> s" and "y \<in> s" and "0 \<le> u" and "0 \<le> v" and "u + v = 1"
  shows "u *\<^sub>R x + v *\<^sub>R y \<in> s"
  using assms unfolding convex_def by fast

lemma convex_alt: "convex s \<longleftrightarrow> (\<forall>x\<in>s. \<forall>y\<in>s. \<forall>u. 0 \<le> u \<and> u \<le> 1 \<longrightarrow> ((1 - u) *\<^sub>R x + u *\<^sub>R y) \<in> s)"
  (is "_ \<longleftrightarrow> ?alt")
proof
  show "convex s" if alt: ?alt
  proof -
    {
      fix x y and u v :: real
      assume mem: "x \<in> s" "y \<in> s"
      assume "0 \<le> u" "0 \<le> v"
      moreover
      assume "u + v = 1"
      then have "u = 1 - v" by auto
      ultimately have "u *\<^sub>R x + v *\<^sub>R y \<in> s"
        using alt [rule_format, OF mem] by auto
    }
    then show ?thesis
      unfolding convex_def by auto
  qed
  show ?alt if "convex s"
    using that by (auto simp: convex_def)
qed

lemma convexD_alt:
  assumes "convex s" "a \<in> s" "b \<in> s" "0 \<le> u" "u \<le> 1"
  shows "((1 - u) *\<^sub>R a + u *\<^sub>R b) \<in> s"
  using assms unfolding convex_alt by auto

lemma mem_convex_alt:
  assumes "convex S" "x \<in> S" "y \<in> S" "u \<ge> 0" "v \<ge> 0" "u + v > 0"
  shows "((u/(u+v)) *\<^sub>R x + (v/(u+v)) *\<^sub>R y) \<in> S"
  apply (rule convexD)
  using assms
       apply (simp_all add: zero_le_divide_iff add_divide_distrib [symmetric])
  done

lemma convex_empty[intro,simp]: "convex {}"
  unfolding convex_def by simp

lemma convex_singleton[intro,simp]: "convex {a}"
  unfolding convex_def by (auto simp: scaleR_left_distrib[symmetric])

lemma convex_UNIV[intro,simp]: "convex UNIV"
  unfolding convex_def by auto

lemma convex_Inter: "(\<And>s. s\<in>f \<Longrightarrow> convex s) \<Longrightarrow> convex(\<Inter>f)"
  unfolding convex_def by auto

lemma convex_Int: "convex s \<Longrightarrow> convex t \<Longrightarrow> convex (s \<inter> t)"
  unfolding convex_def by auto

lemma convex_INT: "(\<And>i. i \<in> A \<Longrightarrow> convex (B i)) \<Longrightarrow> convex (\<Inter>i\<in>A. B i)"
  unfolding convex_def by auto

lemma convex_Times: "convex s \<Longrightarrow> convex t \<Longrightarrow> convex (s \<times> t)"
  unfolding convex_def by auto

lemma convex_halfspace_le: "convex {x. inner a x \<le> b}"
  unfolding convex_def
  by (auto simp: inner_add intro!: convex_bound_le)

lemma convex_halfspace_ge: "convex {x. inner a x \<ge> b}"
proof -
  have *: "{x. inner a x \<ge> b} = {x. inner (-a) x \<le> -b}"
    by auto
  show ?thesis
    unfolding * using convex_halfspace_le[of "-a" "-b"] by auto
qed

lemma convex_halfspace_abs_le: "convex {x. \<bar>inner a x\<bar> \<le> b}"
proof -
  have *: "{x. \<bar>inner a x\<bar> \<le> b} = {x. inner a x \<le> b} \<inter> {x. -b \<le> inner a x}"
    by auto
  show ?thesis
    unfolding * by (simp add: convex_Int convex_halfspace_ge convex_halfspace_le)
qed

lemma convex_hyperplane: "convex {x. inner a x = b}"
proof -
  have *: "{x. inner a x = b} = {x. inner a x \<le> b} \<inter> {x. inner a x \<ge> b}"
    by auto
  show ?thesis using convex_halfspace_le convex_halfspace_ge
    by (auto intro!: convex_Int simp: *)
qed

lemma convex_halfspace_lt: "convex {x. inner a x < b}"
  unfolding convex_def
  by (auto simp: convex_bound_lt inner_add)

lemma convex_halfspace_gt: "convex {x. inner a x > b}"
  using convex_halfspace_lt[of "-a" "-b"] by auto

lemma convex_halfspace_Re_ge: "convex {x. Re x \<ge> b}"
  using convex_halfspace_ge[of b "1::complex"] by simp

lemma convex_halfspace_Re_le: "convex {x. Re x \<le> b}"
  using convex_halfspace_le[of "1::complex" b] by simp

lemma convex_halfspace_Im_ge: "convex {x. Im x \<ge> b}"
  using convex_halfspace_ge[of b \<i>] by simp

lemma convex_halfspace_Im_le: "convex {x. Im x \<le> b}"
  using convex_halfspace_le[of \<i> b] by simp

lemma convex_halfspace_Re_gt: "convex {x. Re x > b}"
  using convex_halfspace_gt[of b "1::complex"] by simp

lemma convex_halfspace_Re_lt: "convex {x. Re x < b}"
  using convex_halfspace_lt[of "1::complex" b] by simp

lemma convex_halfspace_Im_gt: "convex {x. Im x > b}"
  using convex_halfspace_gt[of b \<i>] by simp

lemma convex_halfspace_Im_lt: "convex {x. Im x < b}"
  using convex_halfspace_lt[of \<i> b] by simp

lemma convex_real_interval [iff]:
  fixes a b :: "real"
  shows "convex {a..}" and "convex {..b}"
    and "convex {a<..}" and "convex {..<b}"
    and "convex {a..b}" and "convex {a<..b}"
    and "convex {a..<b}" and "convex {a<..<b}"
proof -
  have "{a..} = {x. a \<le> inner 1 x}"
    by auto
  then show 1: "convex {a..}"
    by (simp only: convex_halfspace_ge)
  have "{..b} = {x. inner 1 x \<le> b}"
    by auto
  then show 2: "convex {..b}"
    by (simp only: convex_halfspace_le)
  have "{a<..} = {x. a < inner 1 x}"
    by auto
  then show 3: "convex {a<..}"
    by (simp only: convex_halfspace_gt)
  have "{..<b} = {x. inner 1 x < b}"
    by auto
  then show 4: "convex {..<b}"
    by (simp only: convex_halfspace_lt)
  have "{a..b} = {a..} \<inter> {..b}"
    by auto
  then show "convex {a..b}"
    by (simp only: convex_Int 1 2)
  have "{a<..b} = {a<..} \<inter> {..b}"
    by auto
  then show "convex {a<..b}"
    by (simp only: convex_Int 3 2)
  have "{a..<b} = {a..} \<inter> {..<b}"
    by auto
  then show "convex {a..<b}"
    by (simp only: convex_Int 1 4)
  have "{a<..<b} = {a<..} \<inter> {..<b}"
    by auto
  then show "convex {a<..<b}"
    by (simp only: convex_Int 3 4)
qed

lemma convex_Reals: "convex \<real>"
  by (simp add: convex_def scaleR_conv_of_real)


subsection\<^marker>\<open>tag unimportant\<close> \<open>Explicit expressions for convexity in terms of arbitrary sums\<close>

lemma convex_sum:
  fixes C :: "'a::real_vector set"
  assumes "finite s"
    and "convex C"
    and "(\<Sum> i \<in> s. a i) = 1"
  assumes "\<And>i. i \<in> s \<Longrightarrow> a i \<ge> 0"
    and "\<And>i. i \<in> s \<Longrightarrow> y i \<in> C"
  shows "(\<Sum> j \<in> s. a j *\<^sub>R y j) \<in> C"
  using assms(1,3,4,5)
proof (induct arbitrary: a set: finite)
  case empty
  then show ?case by simp
next
  case (insert i s) note IH = this(3)
  have "a i + sum a s = 1"
    and "0 \<le> a i"
    and "\<forall>j\<in>s. 0 \<le> a j"
    and "y i \<in> C"
    and "\<forall>j\<in>s. y j \<in> C"
    using insert.hyps(1,2) insert.prems by simp_all
  then have "0 \<le> sum a s"
    by (simp add: sum_nonneg)
  have "a i *\<^sub>R y i + (\<Sum>j\<in>s. a j *\<^sub>R y j) \<in> C"
  proof (cases "sum a s = 0")
    case True
    with \<open>a i + sum a s = 1\<close> have "a i = 1"
      by simp
    from sum_nonneg_0 [OF \<open>finite s\<close> _ True] \<open>\<forall>j\<in>s. 0 \<le> a j\<close> have "\<forall>j\<in>s. a j = 0"
      by simp
    show ?thesis using \<open>a i = 1\<close> and \<open>\<forall>j\<in>s. a j = 0\<close> and \<open>y i \<in> C\<close>
      by simp
  next
    case False
    with \<open>0 \<le> sum a s\<close> have "0 < sum a s"
      by simp
    then have "(\<Sum>j\<in>s. (a j / sum a s) *\<^sub>R y j) \<in> C"
      using \<open>\<forall>j\<in>s. 0 \<le> a j\<close> and \<open>\<forall>j\<in>s. y j \<in> C\<close>
      by (simp add: IH sum_divide_distrib [symmetric])
    from \<open>convex C\<close> and \<open>y i \<in> C\<close> and this and \<open>0 \<le> a i\<close>
      and \<open>0 \<le> sum a s\<close> and \<open>a i + sum a s = 1\<close>
    have "a i *\<^sub>R y i + sum a s *\<^sub>R (\<Sum>j\<in>s. (a j / sum a s) *\<^sub>R y j) \<in> C"
      by (rule convexD)
    then show ?thesis
      by (simp add: scaleR_sum_right False)
  qed
  then show ?case using \<open>finite s\<close> and \<open>i \<notin> s\<close>
    by simp
qed

lemma convex:
  "convex s \<longleftrightarrow> (\<forall>(k::nat) u x. (\<forall>i. 1\<le>i \<and> i\<le>k \<longrightarrow> 0 \<le> u i \<and> x i \<in>s) \<and> (sum u {1..k} = 1)
      \<longrightarrow> sum (\<lambda>i. u i *\<^sub>R x i) {1..k} \<in> s)"
proof safe
  fix k :: nat
  fix u :: "nat \<Rightarrow> real"
  fix x
  assume "convex s"
    "\<forall>i. 1 \<le> i \<and> i \<le> k \<longrightarrow> 0 \<le> u i \<and> x i \<in> s"
    "sum u {1..k} = 1"
  with convex_sum[of "{1 .. k}" s] show "(\<Sum>j\<in>{1 .. k}. u j *\<^sub>R x j) \<in> s"
    by auto
next
  assume *: "\<forall>k u x. (\<forall> i :: nat. 1 \<le> i \<and> i \<le> k \<longrightarrow> 0 \<le> u i \<and> x i \<in> s) \<and> sum u {1..k} = 1
    \<longrightarrow> (\<Sum>i = 1..k. u i *\<^sub>R (x i :: 'a)) \<in> s"
  {
    fix \<mu> :: real
    fix x y :: 'a
    assume xy: "x \<in> s" "y \<in> s"
    assume mu: "\<mu> \<ge> 0" "\<mu> \<le> 1"
    let ?u = "\<lambda>i. if (i :: nat) = 1 then \<mu> else 1 - \<mu>"
    let ?x = "\<lambda>i. if (i :: nat) = 1 then x else y"
    have "{1 :: nat .. 2} \<inter> - {x. x = 1} = {2}"
      by auto
    then have card: "card ({1 :: nat .. 2} \<inter> - {x. x = 1}) = 1"
      by simp
    then have "sum ?u {1 .. 2} = 1"
      using sum.If_cases[of "{(1 :: nat) .. 2}" "\<lambda> x. x = 1" "\<lambda> x. \<mu>" "\<lambda> x. 1 - \<mu>"]
      by auto
    with *[rule_format, of "2" ?u ?x] have s: "(\<Sum>j \<in> {1..2}. ?u j *\<^sub>R ?x j) \<in> s"
      using mu xy by auto
    have grarr: "(\<Sum>j \<in> {Suc (Suc 0)..2}. ?u j *\<^sub>R ?x j) = (1 - \<mu>) *\<^sub>R y"
      using sum.atLeast_Suc_atMost[of "Suc (Suc 0)" 2 "\<lambda> j. (1 - \<mu>) *\<^sub>R y"] by auto
    from sum.atLeast_Suc_atMost[of "Suc 0" 2 "\<lambda> j. ?u j *\<^sub>R ?x j", simplified this]
    have "(\<Sum>j \<in> {1..2}. ?u j *\<^sub>R ?x j) = \<mu> *\<^sub>R x + (1 - \<mu>) *\<^sub>R y"
      by auto
    then have "(1 - \<mu>) *\<^sub>R y + \<mu> *\<^sub>R x \<in> s"
      using s by (auto simp: add.commute)
  }
  then show "convex s"
    unfolding convex_alt by auto
qed


lemma convex_explicit:
  fixes s :: "'a::real_vector set"
  shows "convex s \<longleftrightarrow>
    (\<forall>t u. finite t \<and> t \<subseteq> s \<and> (\<forall>x\<in>t. 0 \<le> u x) \<and> sum u t = 1 \<longrightarrow> sum (\<lambda>x. u x *\<^sub>R x) t \<in> s)"
proof safe
  fix t
  fix u :: "'a \<Rightarrow> real"
  assume "convex s"
    and "finite t"
    and "t \<subseteq> s" "\<forall>x\<in>t. 0 \<le> u x" "sum u t = 1"
  then show "(\<Sum>x\<in>t. u x *\<^sub>R x) \<in> s"
    using convex_sum[of t s u "\<lambda> x. x"] by auto
next
  assume *: "\<forall>t. \<forall> u. finite t \<and> t \<subseteq> s \<and> (\<forall>x\<in>t. 0 \<le> u x) \<and>
    sum u t = 1 \<longrightarrow> (\<Sum>x\<in>t. u x *\<^sub>R x) \<in> s"
  show "convex s"
    unfolding convex_alt
  proof safe
    fix x y
    fix \<mu> :: real
    assume **: "x \<in> s" "y \<in> s" "0 \<le> \<mu>" "\<mu> \<le> 1"
    show "(1 - \<mu>) *\<^sub>R x + \<mu> *\<^sub>R y \<in> s"
    proof (cases "x = y")
      case False
      then show ?thesis
        using *[rule_format, of "{x, y}" "\<lambda> z. if z = x then 1 - \<mu> else \<mu>"] **
        by auto
    next
      case True
      then show ?thesis
        using *[rule_format, of "{x, y}" "\<lambda> z. 1"] **
        by (auto simp: field_simps real_vector.scale_left_diff_distrib)
    qed
  qed
qed

lemma convex_finite:
  assumes "finite s"
  shows "convex s \<longleftrightarrow> (\<forall>u. (\<forall>x\<in>s. 0 \<le> u x) \<and> sum u s = 1 \<longrightarrow> sum (\<lambda>x. u x *\<^sub>R x) s \<in> s)"
  unfolding convex_explicit
  apply safe
  subgoal for u by (erule allE [where x=s], erule allE [where x=u]) auto
  subgoal for t u
  proof -
    have if_distrib_arg: "\<And>P f g x. (if P then f else g) x = (if P then f x else g x)"
      by simp
    assume sum: "\<forall>u. (\<forall>x\<in>s. 0 \<le> u x) \<and> sum u s = 1 \<longrightarrow> (\<Sum>x\<in>s. u x *\<^sub>R x) \<in> s"
    assume *: "\<forall>x\<in>t. 0 \<le> u x" "sum u t = 1"
    assume "t \<subseteq> s"
    then have "s \<inter> t = t" by auto
    with sum[THEN spec[where x="\<lambda>x. if x\<in>t then u x else 0"]] * show "(\<Sum>x\<in>t. u x *\<^sub>R x) \<in> s"
      by (auto simp: assms sum.If_cases if_distrib if_distrib_arg)
  qed
  done


subsection \<open>Convex Functions on a Set\<close>

definition\<^marker>\<open>tag important\<close> convex_on :: "'a::real_vector set \<Rightarrow> ('a \<Rightarrow> real) \<Rightarrow> bool"
  where "convex_on s f \<longleftrightarrow>
    (\<forall>x\<in>s. \<forall>y\<in>s. \<forall>u\<ge>0. \<forall>v\<ge>0. u + v = 1 \<longrightarrow> f (u *\<^sub>R x + v *\<^sub>R y) \<le> u * f x + v * f y)"

lemma convex_onI [intro?]:
  assumes "\<And>t x y. t > 0 \<Longrightarrow> t < 1 \<Longrightarrow> x \<in> A \<Longrightarrow> y \<in> A \<Longrightarrow>
    f ((1 - t) *\<^sub>R x + t *\<^sub>R y) \<le> (1 - t) * f x + t * f y"
  shows "convex_on A f"
  unfolding convex_on_def
proof clarify
  fix x y
  fix u v :: real
  assume A: "x \<in> A" "y \<in> A" "u \<ge> 0" "v \<ge> 0" "u + v = 1"
  from A(5) have [simp]: "v = 1 - u"
    by (simp add: algebra_simps)
  from A(1-4) show "f (u *\<^sub>R x + v *\<^sub>R y) \<le> u * f x + v * f y"
    using assms[of u y x]
    by (cases "u = 0 \<or> u = 1") (auto simp: algebra_simps)
qed

lemma convex_on_linorderI [intro?]:
  fixes A :: "('a::{linorder,real_vector}) set"
  assumes "\<And>t x y. t > 0 \<Longrightarrow> t < 1 \<Longrightarrow> x \<in> A \<Longrightarrow> y \<in> A \<Longrightarrow> x < y \<Longrightarrow>
    f ((1 - t) *\<^sub>R x + t *\<^sub>R y) \<le> (1 - t) * f x + t * f y"
  shows "convex_on A f"
proof
  fix x y
  fix t :: real
  assume A: "x \<in> A" "y \<in> A" "t > 0" "t < 1"
  with assms [of t x y] assms [of "1 - t" y x]
  show "f ((1 - t) *\<^sub>R x + t *\<^sub>R y) \<le> (1 - t) * f x + t * f y"
    by (cases x y rule: linorder_cases) (auto simp: algebra_simps)
qed

lemma convex_onD:
  assumes "convex_on A f"
  shows "\<And>t x y. t \<ge> 0 \<Longrightarrow> t \<le> 1 \<Longrightarrow> x \<in> A \<Longrightarrow> y \<in> A \<Longrightarrow>
    f ((1 - t) *\<^sub>R x + t *\<^sub>R y) \<le> (1 - t) * f x + t * f y"
  using assms by (auto simp: convex_on_def)

lemma convex_onD_Icc:
  assumes "convex_on {x..y} f" "x \<le> (y :: _ :: {real_vector,preorder})"
  shows "\<And>t. t \<ge> 0 \<Longrightarrow> t \<le> 1 \<Longrightarrow>
    f ((1 - t) *\<^sub>R x + t *\<^sub>R y) \<le> (1 - t) * f x + t * f y"
  using assms(2) by (intro convex_onD [OF assms(1)]) simp_all

lemma convex_on_subset: "convex_on t f \<Longrightarrow> s \<subseteq> t \<Longrightarrow> convex_on s f"
  unfolding convex_on_def by auto

lemma convex_on_add [intro]:
  assumes "convex_on s f"
    and "convex_on s g"
  shows "convex_on s (\<lambda>x. f x + g x)"
proof -
  {
    fix x y
    assume "x \<in> s" "y \<in> s"
    moreover
    fix u v :: real
    assume "0 \<le> u" "0 \<le> v" "u + v = 1"
    ultimately
    have "f (u *\<^sub>R x + v *\<^sub>R y) + g (u *\<^sub>R x + v *\<^sub>R y) \<le> (u * f x + v * f y) + (u * g x + v * g y)"
      using assms unfolding convex_on_def by (auto simp: add_mono)
    then have "f (u *\<^sub>R x + v *\<^sub>R y) + g (u *\<^sub>R x + v *\<^sub>R y) \<le> u * (f x + g x) + v * (f y + g y)"
      by (simp add: field_simps)
  }
  then show ?thesis
    unfolding convex_on_def by auto
qed

lemma convex_on_cmul [intro]:
  fixes c :: real
  assumes "0 \<le> c"
    and "convex_on s f"
  shows "convex_on s (\<lambda>x. c * f x)"
proof -
  have *: "u * (c * fx) + v * (c * fy) = c * (u * fx + v * fy)"
    for u c fx v fy :: real
    by (simp add: field_simps)
  show ?thesis using assms(2) and mult_left_mono [OF _ assms(1)]
    unfolding convex_on_def and * by auto
qed

lemma convex_lower:
  assumes "convex_on s f"
    and "x \<in> s"
    and "y \<in> s"
    and "0 \<le> u"
    and "0 \<le> v"
    and "u + v = 1"
  shows "f (u *\<^sub>R x + v *\<^sub>R y) \<le> max (f x) (f y)"
proof -
  let ?m = "max (f x) (f y)"
  have "u * f x + v * f y \<le> u * max (f x) (f y) + v * max (f x) (f y)"
    using assms(4,5) by (auto simp: mult_left_mono add_mono)
  also have "\<dots> = max (f x) (f y)"
    using assms(6) by (simp add: distrib_right [symmetric])
  finally show ?thesis
    using assms unfolding convex_on_def by fastforce
qed

lemma convex_on_dist [intro]:
  fixes s :: "'a::real_normed_vector set"
  shows "convex_on s (\<lambda>x. dist a x)"
proof (auto simp: convex_on_def dist_norm)
  fix x y
  assume "x \<in> s" "y \<in> s"
  fix u v :: real
  assume "0 \<le> u"
  assume "0 \<le> v"
  assume "u + v = 1"
  have "a = u *\<^sub>R a + v *\<^sub>R a"
    unfolding scaleR_left_distrib[symmetric] and \<open>u + v = 1\<close> by simp
  then have *: "a - (u *\<^sub>R x + v *\<^sub>R y) = (u *\<^sub>R (a - x)) + (v *\<^sub>R (a - y))"
    by (auto simp: algebra_simps)
  show "norm (a - (u *\<^sub>R x + v *\<^sub>R y)) \<le> u * norm (a - x) + v * norm (a - y)"
    unfolding * using norm_triangle_ineq[of "u *\<^sub>R (a - x)" "v *\<^sub>R (a - y)"]
    using \<open>0 \<le> u\<close> \<open>0 \<le> v\<close> by auto
qed


subsection\<^marker>\<open>tag unimportant\<close> \<open>Arithmetic operations on sets preserve convexity\<close>

lemma convex_linear_image:
  assumes "linear f"
    and "convex s"
  shows "convex (f ` s)"
proof -
  interpret f: linear f by fact
  from \<open>convex s\<close> show "convex (f ` s)"
    by (simp add: convex_def f.scaleR [symmetric] f.add [symmetric])
qed

lemma convex_linear_vimage:
  assumes "linear f"
    and "convex s"
  shows "convex (f -` s)"
proof -
  interpret f: linear f by fact
  from \<open>convex s\<close> show "convex (f -` s)"
    by (simp add: convex_def f.add f.scaleR)
qed

lemma convex_scaling:
  assumes "convex s"
  shows "convex ((\<lambda>x. c *\<^sub>R x) ` s)"
proof -
  have "linear (\<lambda>x. c *\<^sub>R x)"
    by (simp add: linearI scaleR_add_right)
  then show ?thesis
    using \<open>convex s\<close> by (rule convex_linear_image)
qed

lemma convex_scaled:
  assumes "convex S"
  shows "convex ((\<lambda>x. x *\<^sub>R c) ` S)"
proof -
  have "linear (\<lambda>x. x *\<^sub>R c)"
    by (simp add: linearI scaleR_add_left)
  then show ?thesis
    using \<open>convex S\<close> by (rule convex_linear_image)
qed

lemma convex_negations:
  assumes "convex S"
  shows "convex ((\<lambda>x. - x) ` S)"
proof -
  have "linear (\<lambda>x. - x)"
    by (simp add: linearI)
  then show ?thesis
    using \<open>convex S\<close> by (rule convex_linear_image)
qed

lemma convex_sums:
  assumes "convex S"
    and "convex T"
  shows "convex (\<Union>x\<in> S. \<Union>y \<in> T. {x + y})"
proof -
  have "linear (\<lambda>(x, y). x + y)"
    by (auto intro: linearI simp: scaleR_add_right)
  with assms have "convex ((\<lambda>(x, y). x + y) ` (S \<times> T))"
    by (intro convex_linear_image convex_Times)
  also have "((\<lambda>(x, y). x + y) ` (S \<times> T)) = (\<Union>x\<in> S. \<Union>y \<in> T. {x + y})"
    by auto
  finally show ?thesis .
qed

lemma convex_differences:
  assumes "convex S" "convex T"
  shows "convex (\<Union>x\<in> S. \<Union>y \<in> T. {x - y})"
proof -
  have "{x - y| x y. x \<in> S \<and> y \<in> T} = {x + y |x y. x \<in> S \<and> y \<in> uminus ` T}"
    by (auto simp: diff_conv_add_uminus simp del: add_uminus_conv_diff)
  then show ?thesis
    using convex_sums[OF assms(1) convex_negations[OF assms(2)]] by auto
qed

lemma convex_translation:
  "convex ((+) a ` S)" if "convex S"
proof -
  have "(\<Union> x\<in> {a}. \<Union>y \<in> S. {x + y}) = (+) a ` S"
    by auto
  then show ?thesis
    using convex_sums [OF convex_singleton [of a] that] by auto
qed

lemma convex_translation_subtract:
  "convex ((\<lambda>b. b - a) ` S)" if "convex S"
  using convex_translation [of S "- a"] that by (simp cong: image_cong_simp)

lemma convex_affinity:
  assumes "convex S"
  shows "convex ((\<lambda>x. a + c *\<^sub>R x) ` S)"
proof -
  have "(\<lambda>x. a + c *\<^sub>R x) ` S = (+) a ` (*\<^sub>R) c ` S"
    by auto
  then show ?thesis
    using convex_translation[OF convex_scaling[OF assms], of a c] by auto
qed

lemma convex_on_sum:
  fixes a :: "'a \<Rightarrow> real"
    and y :: "'a \<Rightarrow> 'b::real_vector"
    and f :: "'b \<Rightarrow> real"
  assumes "finite s" "s \<noteq> {}"
    and "convex_on C f"
    and "convex C"
    and "(\<Sum> i \<in> s. a i) = 1"
    and "\<And>i. i \<in> s \<Longrightarrow> a i \<ge> 0"
    and "\<And>i. i \<in> s \<Longrightarrow> y i \<in> C"
  shows "f (\<Sum> i \<in> s. a i *\<^sub>R y i) \<le> (\<Sum> i \<in> s. a i * f (y i))"
  using assms
proof (induct s arbitrary: a rule: finite_ne_induct)
  case (singleton i)
  then have ai: "a i = 1"
    by auto
  then show ?case
    by auto
next
  case (insert i s)
  then have "convex_on C f"
    by simp
  from this[unfolded convex_on_def, rule_format]
  have conv: "\<And>x y \<mu>. x \<in> C \<Longrightarrow> y \<in> C \<Longrightarrow> 0 \<le> \<mu> \<Longrightarrow> \<mu> \<le> 1 \<Longrightarrow>
      f (\<mu> *\<^sub>R x + (1 - \<mu>) *\<^sub>R y) \<le> \<mu> * f x + (1 - \<mu>) * f y"
    by simp
  show ?case
  proof (cases "a i = 1")
    case True
    then have "(\<Sum> j \<in> s. a j) = 0"
      using insert by auto
    then have "\<And>j. j \<in> s \<Longrightarrow> a j = 0"
      using insert by (fastforce simp: sum_nonneg_eq_0_iff)
    then show ?thesis
      using insert by auto
  next
    case False
    from insert have yai: "y i \<in> C" "a i \<ge> 0"
      by auto
    have fis: "finite (insert i s)"
      using insert by auto
    then have ai1: "a i \<le> 1"
      using sum_nonneg_leq_bound[of "insert i s" a] insert by simp
    then have "a i < 1"
      using False by auto
    then have i0: "1 - a i > 0"
      by auto
    let ?a = "\<lambda>j. a j / (1 - a i)"
    have a_nonneg: "?a j \<ge> 0" if "j \<in> s" for j
      using i0 insert that by fastforce
    have "(\<Sum> j \<in> insert i s. a j) = 1"
      using insert by auto
    then have "(\<Sum> j \<in> s. a j) = 1 - a i"
      using sum.insert insert by fastforce
    then have "(\<Sum> j \<in> s. a j) / (1 - a i) = 1"
      using i0 by auto
    then have a1: "(\<Sum> j \<in> s. ?a j) = 1"
      unfolding sum_divide_distrib by simp
    have "convex C" using insert by auto
    then have asum: "(\<Sum> j \<in> s. ?a j *\<^sub>R y j) \<in> C"
      using insert convex_sum [OF \<open>finite s\<close> \<open>convex C\<close> a1 a_nonneg] by auto
    have asum_le: "f (\<Sum> j \<in> s. ?a j *\<^sub>R y j) \<le> (\<Sum> j \<in> s. ?a j * f (y j))"
      using a_nonneg a1 insert by blast
    have "f (\<Sum> j \<in> insert i s. a j *\<^sub>R y j) = f ((\<Sum> j \<in> s. a j *\<^sub>R y j) + a i *\<^sub>R y i)"
      using sum.insert[of s i "\<lambda> j. a j *\<^sub>R y j", OF \<open>finite s\<close> \<open>i \<notin> s\<close>] insert
      by (auto simp only: add.commute)
    also have "\<dots> = f (((1 - a i) * inverse (1 - a i)) *\<^sub>R (\<Sum> j \<in> s. a j *\<^sub>R y j) + a i *\<^sub>R y i)"
      using i0 by auto
    also have "\<dots> = f ((1 - a i) *\<^sub>R (\<Sum> j \<in> s. (a j * inverse (1 - a i)) *\<^sub>R y j) + a i *\<^sub>R y i)"
      using scaleR_right.sum[of "inverse (1 - a i)" "\<lambda> j. a j *\<^sub>R y j" s, symmetric]
      by (auto simp: algebra_simps)
    also have "\<dots> = f ((1 - a i) *\<^sub>R (\<Sum> j \<in> s. ?a j *\<^sub>R y j) + a i *\<^sub>R y i)"
      by (auto simp: divide_inverse)
    also have "\<dots> \<le> (1 - a i) *\<^sub>R f ((\<Sum> j \<in> s. ?a j *\<^sub>R y j)) + a i * f (y i)"
      using conv[of "y i" "(\<Sum> j \<in> s. ?a j *\<^sub>R y j)" "a i", OF yai(1) asum yai(2) ai1]
      by (auto simp: add.commute)
    also have "\<dots> \<le> (1 - a i) * (\<Sum> j \<in> s. ?a j * f (y j)) + a i * f (y i)"
      using add_right_mono [OF mult_left_mono [of _ _ "1 - a i",
            OF asum_le less_imp_le[OF i0]], of "a i * f (y i)"]
      by simp
    also have "\<dots> = (\<Sum> j \<in> s. (1 - a i) * ?a j * f (y j)) + a i * f (y i)"
      unfolding sum_distrib_left[of "1 - a i" "\<lambda> j. ?a j * f (y j)"]
      using i0 by auto
    also have "\<dots> = (\<Sum> j \<in> s. a j * f (y j)) + a i * f (y i)"
      using i0 by auto
    also have "\<dots> = (\<Sum> j \<in> insert i s. a j * f (y j))"
      using insert by auto
    finally show ?thesis
      by simp
  qed
qed

lemma convex_on_alt:
  fixes C :: "'a::real_vector set"
  assumes "convex C"
  shows "convex_on C f \<longleftrightarrow>
    (\<forall>x \<in> C. \<forall> y \<in> C. \<forall> \<mu> :: real. \<mu> \<ge> 0 \<and> \<mu> \<le> 1 \<longrightarrow>
      f (\<mu> *\<^sub>R x + (1 - \<mu>) *\<^sub>R y) \<le> \<mu> * f x + (1 - \<mu>) * f y)"
proof safe
  fix x y
  fix \<mu> :: real
  assume *: "convex_on C f" "x \<in> C" "y \<in> C" "0 \<le> \<mu>" "\<mu> \<le> 1"
  from this[unfolded convex_on_def, rule_format]
  have "0 \<le> u \<Longrightarrow> 0 \<le> v \<Longrightarrow> u + v = 1 \<Longrightarrow> f (u *\<^sub>R x + v *\<^sub>R y) \<le> u * f x + v * f y" for u v
    by auto
  from this [of "\<mu>" "1 - \<mu>", simplified] *
  show "f (\<mu> *\<^sub>R x + (1 - \<mu>) *\<^sub>R y) \<le> \<mu> * f x + (1 - \<mu>) * f y"
    by auto
next
  assume *: "\<forall>x\<in>C. \<forall>y\<in>C. \<forall>\<mu>. 0 \<le> \<mu> \<and> \<mu> \<le> 1 \<longrightarrow>
    f (\<mu> *\<^sub>R x + (1 - \<mu>) *\<^sub>R y) \<le> \<mu> * f x + (1 - \<mu>) * f y"
  {
    fix x y
    fix u v :: real
    assume **: "x \<in> C" "y \<in> C" "u \<ge> 0" "v \<ge> 0" "u + v = 1"
    then have[simp]: "1 - u = v" by auto
    from *[rule_format, of x y u]
    have "f (u *\<^sub>R x + v *\<^sub>R y) \<le> u * f x + v * f y"
      using ** by auto
  }
  then show "convex_on C f"
    unfolding convex_on_def by auto
qed

lemma convex_on_diff:
  fixes f :: "real \<Rightarrow> real"
  assumes f: "convex_on I f"
    and I: "x \<in> I" "y \<in> I"
    and t: "x < t" "t < y"
  shows "(f x - f t) / (x - t) \<le> (f x - f y) / (x - y)"
    and "(f x - f y) / (x - y) \<le> (f t - f y) / (t - y)"
proof -
  define a where "a \<equiv> (t - y) / (x - y)"
  with t have "0 \<le> a" "0 \<le> 1 - a"
    by (auto simp: field_simps)
  with f \<open>x \<in> I\<close> \<open>y \<in> I\<close> have cvx: "f (a * x + (1 - a) * y) \<le> a * f x + (1 - a) * f y"
    by (auto simp: convex_on_def)
  have "a * x + (1 - a) * y = a * (x - y) + y"
    by (simp add: field_simps)
  also have "\<dots> = t"
    unfolding a_def using \<open>x < t\<close> \<open>t < y\<close> by simp
  finally have "f t \<le> a * f x + (1 - a) * f y"
    using cvx by simp
  also have "\<dots> = a * (f x - f y) + f y"
    by (simp add: field_simps)
  finally have "f t - f y \<le> a * (f x - f y)"
    by simp
  with t show "(f x - f t) / (x - t) \<le> (f x - f y) / (x - y)"
    by (simp add: le_divide_eq divide_le_eq field_simps a_def)
  with t show "(f x - f y) / (x - y) \<le> (f t - f y) / (t - y)"
    by (simp add: le_divide_eq divide_le_eq field_simps)
qed

lemma pos_convex_function:
  fixes f :: "real \<Rightarrow> real"
  assumes "convex C"
    and leq: "\<And>x y. x \<in> C \<Longrightarrow> y \<in> C \<Longrightarrow> f' x * (y - x) \<le> f y - f x"
  shows "convex_on C f"
  unfolding convex_on_alt[OF assms(1)]
  using assms
proof safe
  fix x y \<mu> :: real
  let ?x = "\<mu> *\<^sub>R x + (1 - \<mu>) *\<^sub>R y"
  assume *: "convex C" "x \<in> C" "y \<in> C" "\<mu> \<ge> 0" "\<mu> \<le> 1"
  then have "1 - \<mu> \<ge> 0" by auto
  then have xpos: "?x \<in> C"
    using * unfolding convex_alt by fastforce
  have geq: "\<mu> * (f x - f ?x) + (1 - \<mu>) * (f y - f ?x) \<ge>
      \<mu> * f' ?x * (x - ?x) + (1 - \<mu>) * f' ?x * (y - ?x)"
    using add_mono [OF mult_left_mono [OF leq [OF xpos *(2)] \<open>\<mu> \<ge> 0\<close>]
        mult_left_mono [OF leq [OF xpos *(3)] \<open>1 - \<mu> \<ge> 0\<close>]]
    by auto
  then have "\<mu> * f x + (1 - \<mu>) * f y - f ?x \<ge> 0"
    by (auto simp: field_simps)
  then show "f (\<mu> *\<^sub>R x + (1 - \<mu>) *\<^sub>R y) \<le> \<mu> * f x + (1 - \<mu>) * f y"
    using convex_on_alt by auto
qed

lemma atMostAtLeast_subset_convex:
  fixes C :: "real set"
  assumes "convex C"
    and "x \<in> C" "y \<in> C" "x < y"
  shows "{x .. y} \<subseteq> C"
proof safe
  fix z assume z: "z \<in> {x .. y}"
  have less: "z \<in> C" if *: "x < z" "z < y"
  proof -
    let ?\<mu> = "(y - z) / (y - x)"
    have "0 \<le> ?\<mu>" "?\<mu> \<le> 1"
      using assms * by (auto simp: field_simps)
    then have comb: "?\<mu> * x + (1 - ?\<mu>) * y \<in> C"
      using assms iffD1[OF convex_alt, rule_format, of C y x ?\<mu>]
      by (simp add: algebra_simps)
    have "?\<mu> * x + (1 - ?\<mu>) * y = (y - z) * x / (y - x) + (1 - (y - z) / (y - x)) * y"
      by (auto simp: field_simps)
    also have "\<dots> = ((y - z) * x + (y - x - (y - z)) * y) / (y - x)"
      using assms by (simp only: add_divide_distrib) (auto simp: field_simps)
    also have "\<dots> = z"
      using assms by (auto simp: field_simps)
    finally show ?thesis
      using comb by auto
  qed
  show "z \<in> C"
    using z less assms by (auto simp: le_less)
qed

lemma f''_imp_f':
  fixes f :: "real \<Rightarrow> real"
  assumes "convex C"
    and f': "\<And>x. x \<in> C \<Longrightarrow> DERIV f x :> (f' x)"
    and f'': "\<And>x. x \<in> C \<Longrightarrow> DERIV f' x :> (f'' x)"
    and pos: "\<And>x. x \<in> C \<Longrightarrow> f'' x \<ge> 0"
    and x: "x \<in> C"
    and y: "y \<in> C"
  shows "f' x * (y - x) \<le> f y - f x"
  using assms
proof -
  have less_imp: "f y - f x \<ge> f' x * (y - x)" "f' y * (x - y) \<le> f x - f y"
    if *: "x \<in> C" "y \<in> C" "y > x" for x y :: real
  proof -
    from * have ge: "y - x > 0" "y - x \<ge> 0"
      by auto
    from * have le: "x - y < 0" "x - y \<le> 0"
      by auto
    then obtain z1 where z1: "z1 > x" "z1 < y" "f y - f x = (y - x) * f' z1"
      using subsetD[OF atMostAtLeast_subset_convex[OF \<open>convex C\<close> \<open>x \<in> C\<close> \<open>y \<in> C\<close> \<open>x < y\<close>],
          THEN f', THEN MVT2[OF \<open>x < y\<close>, rule_format, unfolded atLeastAtMost_iff[symmetric]]]
      by auto
    then have "z1 \<in> C"
      using atMostAtLeast_subset_convex \<open>convex C\<close> \<open>x \<in> C\<close> \<open>y \<in> C\<close> \<open>x < y\<close>
      by fastforce
    from z1 have z1': "f x - f y = (x - y) * f' z1"
      by (simp add: field_simps)
    obtain z2 where z2: "z2 > x" "z2 < z1" "f' z1 - f' x = (z1 - x) * f'' z2"
      using subsetD[OF atMostAtLeast_subset_convex[OF \<open>convex C\<close> \<open>x \<in> C\<close> \<open>z1 \<in> C\<close> \<open>x < z1\<close>],
          THEN f'', THEN MVT2[OF \<open>x < z1\<close>, rule_format, unfolded atLeastAtMost_iff[symmetric]]] z1
      by auto
    obtain z3 where z3: "z3 > z1" "z3 < y" "f' y - f' z1 = (y - z1) * f'' z3"
      using subsetD[OF atMostAtLeast_subset_convex[OF \<open>convex C\<close> \<open>z1 \<in> C\<close> \<open>y \<in> C\<close> \<open>z1 < y\<close>],
          THEN f'', THEN MVT2[OF \<open>z1 < y\<close>, rule_format, unfolded atLeastAtMost_iff[symmetric]]] z1
      by auto
    have "f' y - (f x - f y) / (x - y) = f' y - f' z1"
      using * z1' by auto
    also have "\<dots> = (y - z1) * f'' z3"
      using z3 by auto
    finally have cool': "f' y - (f x - f y) / (x - y) = (y - z1) * f'' z3"
      by simp
    have A': "y - z1 \<ge> 0"
      using z1 by auto
    have "z3 \<in> C"
      using z3 * atMostAtLeast_subset_convex \<open>convex C\<close> \<open>x \<in> C\<close> \<open>z1 \<in> C\<close> \<open>x < z1\<close>
      by fastforce
    then have B': "f'' z3 \<ge> 0"
      using assms by auto
    from A' B' have "(y - z1) * f'' z3 \<ge> 0"
      by auto
    from cool' this have "f' y - (f x - f y) / (x - y) \<ge> 0"
      by auto
    from mult_right_mono_neg[OF this le(2)]
    have "f' y * (x - y) - (f x - f y) / (x - y) * (x - y) \<le> 0 * (x - y)"
      by (simp add: algebra_simps)
    then have "f' y * (x - y) - (f x - f y) \<le> 0"
      using le by auto
    then have res: "f' y * (x - y) \<le> f x - f y"
      by auto
    have "(f y - f x) / (y - x) - f' x = f' z1 - f' x"
      using * z1 by auto
    also have "\<dots> = (z1 - x) * f'' z2"
      using z2 by auto
    finally have cool: "(f y - f x) / (y - x) - f' x = (z1 - x) * f'' z2"
      by simp
    have A: "z1 - x \<ge> 0"
      using z1 by auto
    have "z2 \<in> C"
      using z2 z1 * atMostAtLeast_subset_convex \<open>convex C\<close> \<open>z1 \<in> C\<close> \<open>y \<in> C\<close> \<open>z1 < y\<close>
      by fastforce
    then have B: "f'' z2 \<ge> 0"
      using assms by auto
    from A B have "(z1 - x) * f'' z2 \<ge> 0"
      by auto
    with cool have "(f y - f x) / (y - x) - f' x \<ge> 0"
      by auto
    from mult_right_mono[OF this ge(2)]
    have "(f y - f x) / (y - x) * (y - x) - f' x * (y - x) \<ge> 0 * (y - x)"
      by (simp add: algebra_simps)
    then have "f y - f x - f' x * (y - x) \<ge> 0"
      using ge by auto
    then show "f y - f x \<ge> f' x * (y - x)" "f' y * (x - y) \<le> f x - f y"
      using res by auto
  qed
  show ?thesis
  proof (cases "x = y")
    case True
    with x y show ?thesis by auto
  next
    case False
    with less_imp x y show ?thesis
      by (auto simp: neq_iff)
  qed
qed

lemma f''_ge0_imp_convex:
  fixes f :: "real \<Rightarrow> real"
  assumes conv: "convex C"
    and f': "\<And>x. x \<in> C \<Longrightarrow> DERIV f x :> (f' x)"
    and f'': "\<And>x. x \<in> C \<Longrightarrow> DERIV f' x :> (f'' x)"
    and pos: "\<And>x. x \<in> C \<Longrightarrow> f'' x \<ge> 0"
  shows "convex_on C f"
  using f''_imp_f'[OF conv f' f'' pos] assms pos_convex_function
  by fastforce

lemma minus_log_convex:
  fixes b :: real
  assumes "b > 1"
  shows "convex_on {0 <..} (\<lambda> x. - log b x)"
proof -
  have "\<And>z. z > 0 \<Longrightarrow> DERIV (log b) z :> 1 / (ln b * z)"
    using DERIV_log by auto
  then have f': "\<And>z. z > 0 \<Longrightarrow> DERIV (\<lambda> z. - log b z) z :> - 1 / (ln b * z)"
    by (auto simp: DERIV_minus)
  have "\<And>z::real. z > 0 \<Longrightarrow> DERIV inverse z :> - (inverse z ^ Suc (Suc 0))"
    using less_imp_neq[THEN not_sym, THEN DERIV_inverse] by auto
  from this[THEN DERIV_cmult, of _ "- 1 / ln b"]
  have "\<And>z::real. z > 0 \<Longrightarrow>
    DERIV (\<lambda> z. (- 1 / ln b) * inverse z) z :> (- 1 / ln b) * (- (inverse z ^ Suc (Suc 0)))"
    by auto
  then have f''0: "\<And>z::real. z > 0 \<Longrightarrow>
    DERIV (\<lambda> z. - 1 / (ln b * z)) z :> 1 / (ln b * z * z)"
    unfolding inverse_eq_divide by (auto simp: mult.assoc)
  have f''_ge0: "\<And>z::real. z > 0 \<Longrightarrow> 1 / (ln b * z * z) \<ge> 0"
    using \<open>b > 1\<close> by (auto intro!: less_imp_le)
  from f''_ge0_imp_convex[OF convex_real_interval(3), unfolded greaterThan_iff, OF f' f''0 f''_ge0]
  show ?thesis
    by auto
qed


subsection\<^marker>\<open>tag unimportant\<close> \<open>Convexity of real functions\<close>

lemma convex_on_realI:
  assumes "connected A"
    and "\<And>x. x \<in> A \<Longrightarrow> (f has_real_derivative f' x) (at x)"
    and "\<And>x y. x \<in> A \<Longrightarrow> y \<in> A \<Longrightarrow> x \<le> y \<Longrightarrow> f' x \<le> f' y"
  shows "convex_on A f"
proof (rule convex_on_linorderI)
  fix t x y :: real
  assume t: "t > 0" "t < 1"
  assume xy: "x \<in> A" "y \<in> A" "x < y"
  define z where "z = (1 - t) * x + t * y"
  with \<open>connected A\<close> and xy have ivl: "{x..y} \<subseteq> A"
    using connected_contains_Icc by blast

  from xy t have xz: "z > x"
    by (simp add: z_def algebra_simps)
  have "y - z = (1 - t) * (y - x)"
    by (simp add: z_def algebra_simps)
  also from xy t have "\<dots> > 0"
    by (intro mult_pos_pos) simp_all
  finally have yz: "z < y"
    by simp

  from assms xz yz ivl t have "\<exists>\<xi>. \<xi> > x \<and> \<xi> < z \<and> f z - f x = (z - x) * f' \<xi>"
    by (intro MVT2) (auto intro!: assms(2))
  then obtain \<xi> where \<xi>: "\<xi> > x" "\<xi> < z" "f' \<xi> = (f z - f x) / (z - x)"
    by auto
  from assms xz yz ivl t have "\<exists>\<eta>. \<eta> > z \<and> \<eta> < y \<and> f y - f z = (y - z) * f' \<eta>"
    by (intro MVT2) (auto intro!: assms(2))
  then obtain \<eta> where \<eta>: "\<eta> > z" "\<eta> < y" "f' \<eta> = (f y - f z) / (y - z)"
    by auto

  from \<eta>(3) have "(f y - f z) / (y - z) = f' \<eta>" ..
  also from \<xi> \<eta> ivl have "\<xi> \<in> A" "\<eta> \<in> A"
    by auto
  with \<xi> \<eta> have "f' \<eta> \<ge> f' \<xi>"
    by (intro assms(3)) auto
  also from \<xi>(3) have "f' \<xi> = (f z - f x) / (z - x)" .
  finally have "(f y - f z) * (z - x) \<ge> (f z - f x) * (y - z)"
    using xz yz by (simp add: field_simps)
  also have "z - x = t * (y - x)"
    by (simp add: z_def algebra_simps)
  also have "y - z = (1 - t) * (y - x)"
    by (simp add: z_def algebra_simps)
  finally have "(f y - f z) * t \<ge> (f z - f x) * (1 - t)"
    using xy by simp
  then show "(1 - t) * f x + t * f y \<ge> f ((1 - t) *\<^sub>R x + t *\<^sub>R y)"
    by (simp add: z_def algebra_simps)
qed

lemma convex_on_inverse:
  assumes "A \<subseteq> {0<..}"
  shows "convex_on A (inverse :: real \<Rightarrow> real)"
proof (rule convex_on_subset[OF _ assms], intro convex_on_realI[of _ _ "\<lambda>x. -inverse (x^2)"])
  fix u v :: real
  assume "u \<in> {0<..}" "v \<in> {0<..}" "u \<le> v"
  with assms show "-inverse (u^2) \<le> -inverse (v^2)"
    by (intro le_imp_neg_le le_imp_inverse_le power_mono) (simp_all)
qed (insert assms, auto intro!: derivative_eq_intros simp: field_split_simps power2_eq_square)

lemma convex_onD_Icc':
  assumes "convex_on {x..y} f" "c \<in> {x..y}"
  defines "d \<equiv> y - x"
  shows "f c \<le> (f y - f x) / d * (c - x) + f x"
proof (cases x y rule: linorder_cases)
  case less
  then have d: "d > 0"
    by (simp add: d_def)
  from assms(2) less have A: "0 \<le> (c - x) / d" "(c - x) / d \<le> 1"
    by (simp_all add: d_def field_split_simps)
  have "f c = f (x + (c - x) * 1)"
    by simp
  also from less have "1 = ((y - x) / d)"
    by (simp add: d_def)
  also from d have "x + (c - x) * \<dots> = (1 - (c - x) / d) *\<^sub>R x + ((c - x) / d) *\<^sub>R y"
    by (simp add: field_simps)
  also have "f \<dots> \<le> (1 - (c - x) / d) * f x + (c - x) / d * f y"
    using assms less by (intro convex_onD_Icc) simp_all
  also from d have "\<dots> = (f y - f x) / d * (c - x) + f x"
    by (simp add: field_simps)
  finally show ?thesis .
qed (insert assms(2), simp_all)

lemma convex_onD_Icc'':
  assumes "convex_on {x..y} f" "c \<in> {x..y}"
  defines "d \<equiv> y - x"
  shows "f c \<le> (f x - f y) / d * (y - c) + f y"
proof (cases x y rule: linorder_cases)
  case less
  then have d: "d > 0"
    by (simp add: d_def)
  from assms(2) less have A: "0 \<le> (y - c) / d" "(y - c) / d \<le> 1"
    by (simp_all add: d_def field_split_simps)
  have "f c = f (y - (y - c) * 1)"
    by simp
  also from less have "1 = ((y - x) / d)"
    by (simp add: d_def)
  also from d have "y - (y - c) * \<dots> = (1 - (1 - (y - c) / d)) *\<^sub>R x + (1 - (y - c) / d) *\<^sub>R y"
    by (simp add: field_simps)
  also have "f \<dots> \<le> (1 - (1 - (y - c) / d)) * f x + (1 - (y - c) / d) * f y"
    using assms less by (intro convex_onD_Icc) (simp_all add: field_simps)
  also from d have "\<dots> = (f x - f y) / d * (y - c) + f y"
    by (simp add: field_simps)
  finally show ?thesis .
qed (insert assms(2), simp_all)

lemma convex_translation_eq [simp]:
  "convex ((+) a ` s) \<longleftrightarrow> convex s"
  by (metis convex_translation translation_galois)

lemma convex_translation_subtract_eq [simp]:
  "convex ((\<lambda>b. b - a) ` s) \<longleftrightarrow> convex s"
  using convex_translation_eq [of "- a"] by (simp cong: image_cong_simp)

lemma convex_linear_image_eq [simp]:
    fixes f :: "'a::real_vector \<Rightarrow> 'b::real_vector"
    shows "\<lbrakk>linear f; inj f\<rbrakk> \<Longrightarrow> convex (f ` s) \<longleftrightarrow> convex s"
    by (metis (no_types) convex_linear_image convex_linear_vimage inj_vimage_image_eq)

lemma fst_snd_linear: "linear (\<lambda>(x,y). x + y)"
  unfolding linear_iff by (simp add: algebra_simps)

lemma vector_choose_size:
  assumes "0 \<le> c"
  obtains x :: "'a::{real_normed_vector, perfect_space}" where "norm x = c"
proof -
  obtain a::'a where "a \<noteq> 0"
    using UNIV_not_singleton UNIV_eq_I set_zero singletonI by fastforce
  then show ?thesis
    by (rule_tac x="scaleR (c / norm a) a" in that) (simp add: assms)
qed

lemma vector_choose_dist:
  assumes "0 \<le> c"
  obtains y :: "'a::{real_normed_vector, perfect_space}" where "dist x y = c"
by (metis add_diff_cancel_left' assms dist_commute dist_norm vector_choose_size)

lemma sum_delta'':
  fixes s::"'a::real_vector set"
  assumes "finite s"
  shows "(\<Sum>x\<in>s. (if y = x then f x else 0) *\<^sub>R x) = (if y\<in>s then (f y) *\<^sub>R y else 0)"
proof -
  have *: "\<And>x y. (if y = x then f x else (0::real)) *\<^sub>R x = (if x=y then (f x) *\<^sub>R x else 0)"
    by auto
  show ?thesis
    unfolding * using sum.delta[OF assms, of y "\<lambda>x. f x *\<^sub>R x"] by auto
qed

lemma dist_triangle_eq:
  fixes x y z :: "'a::real_inner"
  shows "dist x z = dist x y + dist y z \<longleftrightarrow>
    norm (x - y) *\<^sub>R (y - z) = norm (y - z) *\<^sub>R (x - y)"
proof -
  have *: "x - y + (y - z) = x - z" by auto
  show ?thesis unfolding dist_norm norm_triangle_eq[of "x - y" "y - z", unfolded *]
    by (auto simp:norm_minus_commute)
qed




subsection \<open>Cones\<close>

definition\<^marker>\<open>tag important\<close> cone :: "'a::real_vector set \<Rightarrow> bool"
  where "cone s \<longleftrightarrow> (\<forall>x\<in>s. \<forall>c\<ge>0. c *\<^sub>R x \<in> s)"

lemma cone_empty[intro, simp]: "cone {}"
  unfolding cone_def by auto

lemma cone_univ[intro, simp]: "cone UNIV"
  unfolding cone_def by auto

lemma cone_Inter[intro]: "\<forall>s\<in>f. cone s \<Longrightarrow> cone (\<Inter>f)"
  unfolding cone_def by auto

lemma subspace_imp_cone: "subspace S \<Longrightarrow> cone S"
  by (simp add: cone_def subspace_scale)


subsubsection \<open>Conic hull\<close>

lemma cone_cone_hull: "cone (cone hull s)"
  unfolding hull_def by auto

lemma cone_hull_eq: "cone hull s = s \<longleftrightarrow> cone s"
  apply (rule hull_eq)
  using cone_Inter
  unfolding subset_eq
  apply auto
  done

lemma mem_cone:
  assumes "cone S" "x \<in> S" "c \<ge> 0"
  shows "c *\<^sub>R x \<in> S"
  using assms cone_def[of S] by auto

lemma cone_contains_0:
  assumes "cone S"
  shows "S \<noteq> {} \<longleftrightarrow> 0 \<in> S"
proof -
  {
    assume "S \<noteq> {}"
    then obtain a where "a \<in> S" by auto
    then have "0 \<in> S"
      using assms mem_cone[of S a 0] by auto
  }
  then show ?thesis by auto
qed

lemma cone_0: "cone {0}"
  unfolding cone_def by auto

lemma cone_Union[intro]: "(\<forall>s\<in>f. cone s) \<longrightarrow> cone (\<Union>f)"
  unfolding cone_def by blast

lemma cone_iff:
  assumes "S \<noteq> {}"
  shows "cone S \<longleftrightarrow> 0 \<in> S \<and> (\<forall>c. c > 0 \<longrightarrow> ((*\<^sub>R) c) ` S = S)"
proof -
  {
    assume "cone S"
    {
      fix c :: real
      assume "c > 0"
      {
        fix x
        assume "x \<in> S"
        then have "x \<in> ((*\<^sub>R) c) ` S"
          unfolding image_def
          using \<open>cone S\<close> \<open>c>0\<close> mem_cone[of S x "1/c"]
            exI[of "(\<lambda>t. t \<in> S \<and> x = c *\<^sub>R t)" "(1 / c) *\<^sub>R x"]
          by auto
      }
      moreover
      {
        fix x
        assume "x \<in> ((*\<^sub>R) c) ` S"
        then have "x \<in> S"
          using \<open>cone S\<close> \<open>c > 0\<close>
          unfolding cone_def image_def \<open>c > 0\<close> by auto
      }
      ultimately have "((*\<^sub>R) c) ` S = S" by blast
    }
    then have "0 \<in> S \<and> (\<forall>c. c > 0 \<longrightarrow> ((*\<^sub>R) c) ` S = S)"
      using \<open>cone S\<close> cone_contains_0[of S] assms by auto
  }
  moreover
  {
    assume a: "0 \<in> S \<and> (\<forall>c. c > 0 \<longrightarrow> ((*\<^sub>R) c) ` S = S)"
    {
      fix x
      assume "x \<in> S"
      fix c1 :: real
      assume "c1 \<ge> 0"
      then have "c1 = 0 \<or> c1 > 0" by auto
      then have "c1 *\<^sub>R x \<in> S" using a \<open>x \<in> S\<close> by auto
    }
    then have "cone S" unfolding cone_def by auto
  }
  ultimately show ?thesis by blast
qed

lemma cone_hull_empty: "cone hull {} = {}"
  by (metis cone_empty cone_hull_eq)

lemma cone_hull_empty_iff: "S = {} \<longleftrightarrow> cone hull S = {}"
  by (metis bot_least cone_hull_empty hull_subset xtrans(5))

lemma cone_hull_contains_0: "S \<noteq> {} \<longleftrightarrow> 0 \<in> cone hull S"
  using cone_cone_hull[of S] cone_contains_0[of "cone hull S"] cone_hull_empty_iff[of S]
  by auto

lemma mem_cone_hull:
  assumes "x \<in> S" "c \<ge> 0"
  shows "c *\<^sub>R x \<in> cone hull S"
  by (metis assms cone_cone_hull hull_inc mem_cone)

proposition cone_hull_expl: "cone hull S = {c *\<^sub>R x | c x. c \<ge> 0 \<and> x \<in> S}"
  (is "?lhs = ?rhs")
proof -
  {
    fix x
    assume "x \<in> ?rhs"
    then obtain cx :: real and xx where x: "x = cx *\<^sub>R xx" "cx \<ge> 0" "xx \<in> S"
      by auto
    fix c :: real
    assume c: "c \<ge> 0"
    then have "c *\<^sub>R x = (c * cx) *\<^sub>R xx"
      using x by (simp add: algebra_simps)
    moreover
    have "c * cx \<ge> 0" using c x by auto
    ultimately
    have "c *\<^sub>R x \<in> ?rhs" using x by auto
  }
  then have "cone ?rhs"
    unfolding cone_def by auto
  then have "?rhs \<in> Collect cone"
    unfolding mem_Collect_eq by auto
  {
    fix x
    assume "x \<in> S"
    then have "1 *\<^sub>R x \<in> ?rhs"
      apply auto
      apply (rule_tac x = 1 in exI, auto)
      done
    then have "x \<in> ?rhs" by auto
  }
  then have "S \<subseteq> ?rhs" by auto
  then have "?lhs \<subseteq> ?rhs"
    using \<open>?rhs \<in> Collect cone\<close> hull_minimal[of S "?rhs" "cone"] by auto
  moreover
  {
    fix x
    assume "x \<in> ?rhs"
    then obtain cx :: real and xx where x: "x = cx *\<^sub>R xx" "cx \<ge> 0" "xx \<in> S"
      by auto
    then have "xx \<in> cone hull S"
      using hull_subset[of S] by auto
    then have "x \<in> ?lhs"
      using x cone_cone_hull[of S] cone_def[of "cone hull S"] by auto
  }
  ultimately show ?thesis by auto
qed

lemma convex_cone:
  "convex s \<and> cone s \<longleftrightarrow> (\<forall>x\<in>s. \<forall>y\<in>s. (x + y) \<in> s) \<and> (\<forall>x\<in>s. \<forall>c\<ge>0. (c *\<^sub>R x) \<in> s)"
  (is "?lhs = ?rhs")
proof -
  {
    fix x y
    assume "x\<in>s" "y\<in>s" and ?lhs
    then have "2 *\<^sub>R x \<in>s" "2 *\<^sub>R y \<in> s"
      unfolding cone_def by auto
    then have "x + y \<in> s"
      using \<open>?lhs\<close>[unfolded convex_def, THEN conjunct1]
      apply (erule_tac x="2*\<^sub>R x" in ballE)
      apply (erule_tac x="2*\<^sub>R y" in ballE)
      apply (erule_tac x="1/2" in allE, simp)
      apply (erule_tac x="1/2" in allE, auto)
      done
  }
  then show ?thesis
    unfolding convex_def cone_def by blast
qed


subsection\<^marker>\<open>tag unimportant\<close> \<open>Connectedness of convex sets\<close>

lemma convex_connected:
  fixes S :: "'a::real_normed_vector set"
  assumes "convex S"
  shows "connected S"
proof (rule connectedI)
  fix A B
  assume "open A" "open B" "A \<inter> B \<inter> S = {}" "S \<subseteq> A \<union> B"
  moreover
  assume "A \<inter> S \<noteq> {}" "B \<inter> S \<noteq> {}"
  then obtain a b where a: "a \<in> A" "a \<in> S" and b: "b \<in> B" "b \<in> S" by auto
  define f where [abs_def]: "f u = u *\<^sub>R a + (1 - u) *\<^sub>R b" for u
  then have "continuous_on {0 .. 1} f"
    by (auto intro!: continuous_intros)
  then have "connected (f ` {0 .. 1})"
    by (auto intro!: connected_continuous_image)
  note connectedD[OF this, of A B]
  moreover have "a \<in> A \<inter> f ` {0 .. 1}"
    using a by (auto intro!: image_eqI[of _ _ 1] simp: f_def)
  moreover have "b \<in> B \<inter> f ` {0 .. 1}"
    using b by (auto intro!: image_eqI[of _ _ 0] simp: f_def)
  moreover have "f ` {0 .. 1} \<subseteq> S"
    using \<open>convex S\<close> a b unfolding convex_def f_def by auto
  ultimately show False by auto
qed

corollary%unimportant connected_UNIV[intro]: "connected (UNIV :: 'a::real_normed_vector set)"
by (simp add: convex_connected)

lemma convex_prod:
  assumes "\<And>i. i \<in> Basis \<Longrightarrow> convex {x. P i x}"
  shows "convex {x. \<forall>i\<in>Basis. P i (x\<bullet>i)}"
  using assms unfolding convex_def
  by (auto simp: inner_add_left)

lemma convex_positive_orthant: "convex {x::'a::euclidean_space. (\<forall>i\<in>Basis. 0 \<le> x\<bullet>i)}"
by (rule convex_prod) (simp flip: atLeast_def)

subsection \<open>Convex hull\<close>

lemma convex_convex_hull [iff]: "convex (convex hull s)"
  unfolding hull_def
  using convex_Inter[of "{t. convex t \<and> s \<subseteq> t}"]
  by auto

lemma convex_hull_subset:
    "s \<subseteq> convex hull t \<Longrightarrow> convex hull s \<subseteq> convex hull t"
  by (simp add: subset_hull)

lemma convex_hull_eq: "convex hull s = s \<longleftrightarrow> convex s"
  by (metis convex_convex_hull hull_same)

subsubsection\<^marker>\<open>tag unimportant\<close> \<open>Convex hull is "preserved" by a linear function\<close>

lemma convex_hull_linear_image:
  assumes f: "linear f"
  shows "f ` (convex hull s) = convex hull (f ` s)"
proof
  show "convex hull (f ` s) \<subseteq> f ` (convex hull s)"
    by (intro hull_minimal image_mono hull_subset convex_linear_image assms convex_convex_hull)
  show "f ` (convex hull s) \<subseteq> convex hull (f ` s)"
  proof (unfold image_subset_iff_subset_vimage, rule hull_minimal)
    show "s \<subseteq> f -` (convex hull (f ` s))"
      by (fast intro: hull_inc)
    show "convex (f -` (convex hull (f ` s)))"
      by (intro convex_linear_vimage [OF f] convex_convex_hull)
  qed
qed

lemma in_convex_hull_linear_image:
  assumes "linear f"
    and "x \<in> convex hull s"
  shows "f x \<in> convex hull (f ` s)"
  using convex_hull_linear_image[OF assms(1)] assms(2) by auto

lemma convex_hull_Times:
  "convex hull (s \<times> t) = (convex hull s) \<times> (convex hull t)"
proof
  show "convex hull (s \<times> t) \<subseteq> (convex hull s) \<times> (convex hull t)"
    by (intro hull_minimal Sigma_mono hull_subset convex_Times convex_convex_hull)
  have "(x, y) \<in> convex hull (s \<times> t)" if x: "x \<in> convex hull s" and y: "y \<in> convex hull t" for x y
  proof (rule hull_induct [OF x], rule hull_induct [OF y])
    fix x y assume "x \<in> s" and "y \<in> t"
    then show "(x, y) \<in> convex hull (s \<times> t)"
      by (simp add: hull_inc)
  next
    fix x let ?S = "((\<lambda>y. (0, y)) -` (\<lambda>p. (- x, 0) + p) ` (convex hull s \<times> t))"
    have "convex ?S"
      by (intro convex_linear_vimage convex_translation convex_convex_hull,
        simp add: linear_iff)
    also have "?S = {y. (x, y) \<in> convex hull (s \<times> t)}"
      by (auto simp: image_def Bex_def)
    finally show "convex {y. (x, y) \<in> convex hull (s \<times> t)}" .
  next
    show "convex {x. (x, y) \<in> convex hull s \<times> t}"
    proof -
      fix y let ?S = "((\<lambda>x. (x, 0)) -` (\<lambda>p. (0, - y) + p) ` (convex hull s \<times> t))"
      have "convex ?S"
      by (intro convex_linear_vimage convex_translation convex_convex_hull,
        simp add: linear_iff)
      also have "?S = {x. (x, y) \<in> convex hull (s \<times> t)}"
        by (auto simp: image_def Bex_def)
      finally show "convex {x. (x, y) \<in> convex hull (s \<times> t)}" .
    qed
  qed
  then show "(convex hull s) \<times> (convex hull t) \<subseteq> convex hull (s \<times> t)"
    unfolding subset_eq split_paired_Ball_Sigma by blast
qed


subsubsection\<^marker>\<open>tag unimportant\<close> \<open>Stepping theorems for convex hulls of finite sets\<close>

lemma convex_hull_empty[simp]: "convex hull {} = {}"
  by (rule hull_unique) auto

lemma convex_hull_singleton[simp]: "convex hull {a} = {a}"
  by (rule hull_unique) auto

lemma convex_hull_insert:
  fixes S :: "'a::real_vector set"
  assumes "S \<noteq> {}"
  shows "convex hull (insert a S) =
         {x. \<exists>u\<ge>0. \<exists>v\<ge>0. \<exists>b. (u + v = 1) \<and> b \<in> (convex hull S) \<and> (x = u *\<^sub>R a + v *\<^sub>R b)}"
  (is "_ = ?hull")
proof (intro equalityI hull_minimal subsetI)
  fix x
  assume "x \<in> insert a S"
  then have "\<exists>u\<ge>0. \<exists>v\<ge>0. u + v = 1 \<and> (\<exists>b. b \<in> convex hull S \<and> x = u *\<^sub>R a + v *\<^sub>R b)"
  unfolding insert_iff
  proof
    assume "x = a"
    then show ?thesis
      by (rule_tac x=1 in exI) (use assms hull_subset in fastforce)
  next
    assume "x \<in> S"
    with hull_subset[of S convex] show ?thesis
      by force
  qed
  then show "x \<in> ?hull"
    by simp
next
  fix x
  assume "x \<in> ?hull"
  then obtain u v b where obt: "u\<ge>0" "v\<ge>0" "u + v = 1" "b \<in> convex hull S" "x = u *\<^sub>R a + v *\<^sub>R b"
    by auto
  have "a \<in> convex hull insert a S" "b \<in> convex hull insert a S"
    using hull_mono[of S "insert a S" convex] hull_mono[of "{a}" "insert a S" convex] and obt(4)
    by auto
  then show "x \<in> convex hull insert a S"
    unfolding obt(5) using obt(1-3)
    by (rule convexD [OF convex_convex_hull])
next
  show "convex ?hull"
  proof (rule convexI)
    fix x y u v
    assume as: "(0::real) \<le> u" "0 \<le> v" "u + v = 1" and x: "x \<in> ?hull" and y: "y \<in> ?hull"
    from x obtain u1 v1 b1 where
      obt1: "u1\<ge>0" "v1\<ge>0" "u1 + v1 = 1" "b1 \<in> convex hull S" and xeq: "x = u1 *\<^sub>R a + v1 *\<^sub>R b1"
      by auto
    from y obtain u2 v2 b2 where
      obt2: "u2\<ge>0" "v2\<ge>0" "u2 + v2 = 1" "b2 \<in> convex hull S" and yeq: "y = u2 *\<^sub>R a + v2 *\<^sub>R b2"
      by auto
    have *: "\<And>(x::'a) s1 s2. x - s1 *\<^sub>R x - s2 *\<^sub>R x = ((1::real) - (s1 + s2)) *\<^sub>R x"
      by (auto simp: algebra_simps)
    have "\<exists>b \<in> convex hull S. u *\<^sub>R x + v *\<^sub>R y =
      (u * u1) *\<^sub>R a + (v * u2) *\<^sub>R a + (b - (u * u1) *\<^sub>R b - (v * u2) *\<^sub>R b)"
    proof (cases "u * v1 + v * v2 = 0")
      case True
      have *: "\<And>(x::'a) s1 s2. x - s1 *\<^sub>R x - s2 *\<^sub>R x = ((1::real) - (s1 + s2)) *\<^sub>R x"
        by (auto simp: algebra_simps)
      have eq0: "u * v1 = 0" "v * v2 = 0"
        using True mult_nonneg_nonneg[OF \<open>u\<ge>0\<close> \<open>v1\<ge>0\<close>] mult_nonneg_nonneg[OF \<open>v\<ge>0\<close> \<open>v2\<ge>0\<close>]
        by arith+
      then have "u * u1 + v * u2 = 1"
        using as(3) obt1(3) obt2(3) by auto
      then show ?thesis
        using "*" eq0 as obt1(4) xeq yeq by auto
    next
      case False
      have "1 - (u * u1 + v * u2) = (u + v) - (u * u1 + v * u2)"
        using as(3) obt1(3) obt2(3) by (auto simp: field_simps)
      also have "\<dots> = u * (v1 + u1 - u1) + v * (v2 + u2 - u2)"
        using as(3) obt1(3) obt2(3) by (auto simp: field_simps)
      also have "\<dots> = u * v1 + v * v2"
        by simp
      finally have **:"1 - (u * u1 + v * u2) = u * v1 + v * v2" by auto
      let ?b = "((u * v1) / (u * v1 + v * v2)) *\<^sub>R b1 + ((v * v2) / (u * v1 + v * v2)) *\<^sub>R b2"
      have zeroes: "0 \<le> u * v1 + v * v2" "0 \<le> u * v1" "0 \<le> u * v1 + v * v2" "0 \<le> v * v2"
        using as(1,2) obt1(1,2) obt2(1,2) by auto
      show ?thesis
      proof
        show "u *\<^sub>R x + v *\<^sub>R y = (u * u1) *\<^sub>R a + (v * u2) *\<^sub>R a + (?b - (u * u1) *\<^sub>R ?b - (v * u2) *\<^sub>R ?b)"
          unfolding xeq yeq * **
          using False by (auto simp: scaleR_left_distrib scaleR_right_distrib)
        show "?b \<in> convex hull S"
          using False zeroes obt1(4) obt2(4)
          by (auto simp: convexD [OF convex_convex_hull] scaleR_left_distrib scaleR_right_distrib  add_divide_distrib[symmetric]  zero_le_divide_iff)
      qed
    qed
    then obtain b where b: "b \<in> convex hull S" 
       "u *\<^sub>R x + v *\<^sub>R y = (u * u1) *\<^sub>R a + (v * u2) *\<^sub>R a + (b - (u * u1) *\<^sub>R b - (v * u2) *\<^sub>R b)" ..

    have u1: "u1 \<le> 1"
      unfolding obt1(3)[symmetric] and not_le using obt1(2) by auto
    have u2: "u2 \<le> 1"
      unfolding obt2(3)[symmetric] and not_le using obt2(2) by auto
    have "u1 * u + u2 * v \<le> max u1 u2 * u + max u1 u2 * v"
    proof (rule add_mono)
      show "u1 * u \<le> max u1 u2 * u" "u2 * v \<le> max u1 u2 * v"
        by (simp_all add: as mult_right_mono)
    qed
    also have "\<dots> \<le> 1"
      unfolding distrib_left[symmetric] and as(3) using u1 u2 by auto
    finally have le1: "u1 * u + u2 * v \<le> 1" .    
    show "u *\<^sub>R x + v *\<^sub>R y \<in> ?hull"
    proof (intro CollectI exI conjI)
      show "0 \<le> u * u1 + v * u2"
        by (simp add: as(1) as(2) obt1(1) obt2(1))
      show "0 \<le> 1 - u * u1 - v * u2"
        by (simp add: le1 diff_diff_add mult.commute)
    qed (use b in \<open>auto simp: algebra_simps\<close>)
  qed
qed

lemma convex_hull_insert_alt:
   "convex hull (insert a S) =
     (if S = {} then {a}
      else {(1 - u) *\<^sub>R a + u *\<^sub>R x |x u. 0 \<le> u \<and> u \<le> 1 \<and> x \<in> convex hull S})"
  apply (auto simp: convex_hull_insert)
  using diff_eq_eq apply fastforce
  by (metis add.group_left_neutral add_le_imp_le_diff diff_add_cancel)

subsubsection\<^marker>\<open>tag unimportant\<close> \<open>Explicit expression for convex hull\<close>

proposition convex_hull_indexed:
  fixes S :: "'a::real_vector set"
  shows "convex hull S =
    {y. \<exists>k u x. (\<forall>i\<in>{1::nat .. k}. 0 \<le> u i \<and> x i \<in> S) \<and>
                (sum u {1..k} = 1) \<and> (\<Sum>i = 1..k. u i *\<^sub>R x i) = y}"
    (is "?xyz = ?hull")
proof (rule hull_unique [OF _ convexI])
  show "S \<subseteq> ?hull" 
    by (clarsimp, rule_tac x=1 in exI, rule_tac x="\<lambda>x. 1" in exI, auto)
next
  fix T
  assume "S \<subseteq> T" "convex T"
  then show "?hull \<subseteq> T"
    by (blast intro: convex_sum)
next
  fix x y u v
  assume uv: "0 \<le> u" "0 \<le> v" "u + v = (1::real)"
  assume xy: "x \<in> ?hull" "y \<in> ?hull"
  from xy obtain k1 u1 x1 where
    x [rule_format]: "\<forall>i\<in>{1::nat..k1}. 0\<le>u1 i \<and> x1 i \<in> S" 
                      "sum u1 {Suc 0..k1} = 1" "(\<Sum>i = Suc 0..k1. u1 i *\<^sub>R x1 i) = x"
    by auto
  from xy obtain k2 u2 x2 where
    y [rule_format]: "\<forall>i\<in>{1::nat..k2}. 0\<le>u2 i \<and> x2 i \<in> S" 
                     "sum u2 {Suc 0..k2} = 1" "(\<Sum>i = Suc 0..k2. u2 i *\<^sub>R x2 i) = y"
    by auto
  have *: "\<And>P (x::'a) y s t i. (if P i then s else t) *\<^sub>R (if P i then x else y) = (if P i then s *\<^sub>R x else t *\<^sub>R y)"
          "{1..k1 + k2} \<inter> {1..k1} = {1..k1}" "{1..k1 + k2} \<inter> - {1..k1} = (\<lambda>i. i + k1) ` {1..k2}"
    by auto
  have inj: "inj_on (\<lambda>i. i + k1) {1..k2}"
    unfolding inj_on_def by auto
  let ?uu = "\<lambda>i. if i \<in> {1..k1} then u * u1 i else v * u2 (i - k1)"
  let ?xx = "\<lambda>i. if i \<in> {1..k1} then x1 i else x2 (i - k1)"
  show "u *\<^sub>R x + v *\<^sub>R y \<in> ?hull"
  proof (intro CollectI exI conjI ballI)
    show "0 \<le> ?uu i" "?xx i \<in> S" if "i \<in> {1..k1+k2}" for i
      using that by (auto simp add: le_diff_conv uv(1) x(1) uv(2) y(1))
    show "(\<Sum>i = 1..k1 + k2. ?uu i) = 1"  "(\<Sum>i = 1..k1 + k2. ?uu i *\<^sub>R ?xx i) = u *\<^sub>R x + v *\<^sub>R y"
      unfolding * sum.If_cases[OF finite_atLeastAtMost[of 1 "k1 + k2"]]
        sum.reindex[OF inj] Collect_mem_eq o_def
      unfolding scaleR_scaleR[symmetric] scaleR_right.sum [symmetric] sum_distrib_left[symmetric]
      by (simp_all add: sum_distrib_left[symmetric]  x(2,3) y(2,3) uv(3))
  qed 
qed

lemma convex_hull_finite:
  fixes S :: "'a::real_vector set"
  assumes "finite S"
  shows "convex hull S = {y. \<exists>u. (\<forall>x\<in>S. 0 \<le> u x) \<and> sum u S = 1 \<and> sum (\<lambda>x. u x *\<^sub>R x) S = y}"
  (is "?HULL = _")
proof (rule hull_unique [OF _ convexI]; clarify)
  fix x
  assume "x \<in> S"
  then show "\<exists>u. (\<forall>x\<in>S. 0 \<le> u x) \<and> sum u S = 1 \<and> (\<Sum>x\<in>S. u x *\<^sub>R x) = x"
    by (rule_tac x="\<lambda>y. if x=y then 1 else 0" in exI) (auto simp: sum.delta'[OF assms] sum_delta''[OF assms])
next
  fix u v :: real
  assume uv: "0 \<le> u" "0 \<le> v" "u + v = 1"
  fix ux assume ux [rule_format]: "\<forall>x\<in>S. 0 \<le> ux x" "sum ux S = (1::real)"
  fix uy assume uy [rule_format]: "\<forall>x\<in>S. 0 \<le> uy x" "sum uy S = (1::real)"
  have "0 \<le> u * ux x + v * uy x" if "x\<in>S" for x
    by (simp add: that uv ux(1) uy(1))
  moreover
  have "(\<Sum>x\<in>S. u * ux x + v * uy x) = 1"
    unfolding sum.distrib and sum_distrib_left[symmetric] ux(2) uy(2)
    using uv(3) by auto
  moreover
  have "(\<Sum>x\<in>S. (u * ux x + v * uy x) *\<^sub>R x) = u *\<^sub>R (\<Sum>x\<in>S. ux x *\<^sub>R x) + v *\<^sub>R (\<Sum>x\<in>S. uy x *\<^sub>R x)"
    unfolding scaleR_left_distrib sum.distrib scaleR_scaleR[symmetric] scaleR_right.sum [symmetric]
    by auto
  ultimately
  show "\<exists>uc. (\<forall>x\<in>S. 0 \<le> uc x) \<and> sum uc S = 1 \<and>
             (\<Sum>x\<in>S. uc x *\<^sub>R x) = u *\<^sub>R (\<Sum>x\<in>S. ux x *\<^sub>R x) + v *\<^sub>R (\<Sum>x\<in>S. uy x *\<^sub>R x)"
    by (rule_tac x="\<lambda>x. u * ux x + v * uy x" in exI, auto)
qed (use assms in \<open>auto simp: convex_explicit\<close>)


subsubsection\<^marker>\<open>tag unimportant\<close> \<open>Another formulation\<close>

text "Formalized by Lars Schewe."

lemma convex_hull_explicit:
  fixes p :: "'a::real_vector set"
  shows "convex hull p =
    {y. \<exists>S u. finite S \<and> S \<subseteq> p \<and> (\<forall>x\<in>S. 0 \<le> u x) \<and> sum u S = 1 \<and> sum (\<lambda>v. u v *\<^sub>R v) S = y}"
  (is "?lhs = ?rhs")
proof -
  {
    fix x
    assume "x\<in>?lhs"
    then obtain k u y where
        obt: "\<forall>i\<in>{1::nat..k}. 0 \<le> u i \<and> y i \<in> p" "sum u {1..k} = 1" "(\<Sum>i = 1..k. u i *\<^sub>R y i) = x"
      unfolding convex_hull_indexed by auto

    have fin: "finite {1..k}" by auto
    have fin': "\<And>v. finite {i \<in> {1..k}. y i = v}" by auto
    {
      fix j
      assume "j\<in>{1..k}"
      then have "y j \<in> p" "0 \<le> sum u {i. Suc 0 \<le> i \<and> i \<le> k \<and> y i = y j}"
        using obt(1)[THEN bspec[where x=j]] and obt(2)
        apply simp
        apply (rule sum_nonneg)
        using obt(1)
        apply auto
        done
    }
    moreover
    have "(\<Sum>v\<in>y ` {1..k}. sum u {i \<in> {1..k}. y i = v}) = 1"
      unfolding sum.image_gen[OF fin, symmetric] using obt(2) by auto
    moreover have "(\<Sum>v\<in>y ` {1..k}. sum u {i \<in> {1..k}. y i = v} *\<^sub>R v) = x"
      using sum.image_gen[OF fin, of "\<lambda>i. u i *\<^sub>R y i" y, symmetric]
      unfolding scaleR_left.sum using obt(3) by auto
    ultimately
    have "\<exists>S u. finite S \<and> S \<subseteq> p \<and> (\<forall>x\<in>S. 0 \<le> u x) \<and> sum u S = 1 \<and> (\<Sum>v\<in>S. u v *\<^sub>R v) = x"
      apply (rule_tac x="y ` {1..k}" in exI)
      apply (rule_tac x="\<lambda>v. sum u {i\<in>{1..k}. y i = v}" in exI, auto)
      done
    then have "x\<in>?rhs" by auto
  }
  moreover
  {
    fix y
    assume "y\<in>?rhs"
    then obtain S u where
      obt: "finite S" "S \<subseteq> p" "\<forall>x\<in>S. 0 \<le> u x" "sum u S = 1" "(\<Sum>v\<in>S. u v *\<^sub>R v) = y"
      by auto

    obtain f where f: "inj_on f {1..card S}" "f ` {1..card S} = S"
      using ex_bij_betw_nat_finite_1[OF obt(1)] unfolding bij_betw_def by auto

    {
      fix i :: nat
      assume "i\<in>{1..card S}"
      then have "f i \<in> S"
        using f(2) by blast
      then have "0 \<le> u (f i)" "f i \<in> p" using obt(2,3) by auto
    }
    moreover have *: "finite {1..card S}" by auto
    {
      fix y
      assume "y\<in>S"
      then obtain i where "i\<in>{1..card S}" "f i = y"
        using f using image_iff[of y f "{1..card S}"]
        by auto
      then have "{x. Suc 0 \<le> x \<and> x \<le> card S \<and> f x = y} = {i}"
        apply auto
        using f(1)[unfolded inj_on_def]
        by (metis One_nat_def atLeastAtMost_iff)
      then have "card {x. Suc 0 \<le> x \<and> x \<le> card S \<and> f x = y} = 1" by auto
      then have "(\<Sum>x\<in>{x \<in> {1..card S}. f x = y}. u (f x)) = u y"
          "(\<Sum>x\<in>{x \<in> {1..card S}. f x = y}. u (f x) *\<^sub>R f x) = u y *\<^sub>R y"
        by (auto simp: sum_constant_scaleR)
    }
    then have "(\<Sum>x = 1..card S. u (f x)) = 1" "(\<Sum>i = 1..card S. u (f i) *\<^sub>R f i) = y"
      unfolding sum.image_gen[OF *(1), of "\<lambda>x. u (f x) *\<^sub>R f x" f]
        and sum.image_gen[OF *(1), of "\<lambda>x. u (f x)" f]
      unfolding f
      using sum.cong [of S S "\<lambda>y. (\<Sum>x\<in>{x \<in> {1..card S}. f x = y}. u (f x) *\<^sub>R f x)" "\<lambda>v. u v *\<^sub>R v"]
      using sum.cong [of S S "\<lambda>y. (\<Sum>x\<in>{x \<in> {1..card S}. f x = y}. u (f x))" u]
      unfolding obt(4,5)
      by auto
    ultimately
    have "\<exists>k u x. (\<forall>i\<in>{1..k}. 0 \<le> u i \<and> x i \<in> p) \<and> sum u {1..k} = 1 \<and>
        (\<Sum>i::nat = 1..k. u i *\<^sub>R x i) = y"
      apply (rule_tac x="card S" in exI)
      apply (rule_tac x="u \<circ> f" in exI)
      apply (rule_tac x=f in exI, fastforce)
      done
    then have "y \<in> ?lhs"
      unfolding convex_hull_indexed by auto
  }
  ultimately show ?thesis
    unfolding set_eq_iff by blast
qed


subsubsection\<^marker>\<open>tag unimportant\<close> \<open>A stepping theorem for that expansion\<close>

lemma convex_hull_finite_step:
  fixes S :: "'a::real_vector set"
  assumes "finite S"
  shows
    "(\<exists>u. (\<forall>x\<in>insert a S. 0 \<le> u x) \<and> sum u (insert a S) = w \<and> sum (\<lambda>x. u x *\<^sub>R x) (insert a S) = y)
      \<longleftrightarrow> (\<exists>v\<ge>0. \<exists>u. (\<forall>x\<in>S. 0 \<le> u x) \<and> sum u S = w - v \<and> sum (\<lambda>x. u x *\<^sub>R x) S = y - v *\<^sub>R a)"
  (is "?lhs = ?rhs")
proof (rule, case_tac[!] "a\<in>S")
  assume "a \<in> S"
  then have *: "insert a S = S" by auto
  assume ?lhs
  then show ?rhs
    unfolding *  by (rule_tac x=0 in exI, auto)
next
  assume ?lhs
  then obtain u where
      u: "\<forall>x\<in>insert a S. 0 \<le> u x" "sum u (insert a S) = w" "(\<Sum>x\<in>insert a S. u x *\<^sub>R x) = y"
    by auto
  assume "a \<notin> S"
  then show ?rhs
    apply (rule_tac x="u a" in exI)
    using u(1)[THEN bspec[where x=a]]
    apply simp
    apply (rule_tac x=u in exI)
    using u[unfolded sum_clauses(2)[OF assms]] and \<open>a\<notin>S\<close>
    apply auto
    done
next
  assume "a \<in> S"
  then have *: "insert a S = S" by auto
  have fin: "finite (insert a S)" using assms by auto
  assume ?rhs
  then obtain v u where uv: "v\<ge>0" "\<forall>x\<in>S. 0 \<le> u x" "sum u S = w - v" "(\<Sum>x\<in>S. u x *\<^sub>R x) = y - v *\<^sub>R a"
    by auto
  show ?lhs
    apply (rule_tac x = "\<lambda>x. (if a = x then v else 0) + u x" in exI)
    unfolding scaleR_left_distrib and sum.distrib and sum_delta''[OF fin] and sum.delta'[OF fin]
    unfolding sum_clauses(2)[OF assms]
    using uv and uv(2)[THEN bspec[where x=a]] and \<open>a\<in>S\<close>
    apply auto
    done
next
  assume ?rhs
  then obtain v u where uv: "v\<ge>0" "\<forall>x\<in>S. 0 \<le> u x" "sum u S = w - v" "(\<Sum>x\<in>S. u x *\<^sub>R x) = y - v *\<^sub>R a"
    by auto
  moreover assume "a \<notin> S"
  moreover
  have "(\<Sum>x\<in>S. if a = x then v else u x) = sum u S"  "(\<Sum>x\<in>S. (if a = x then v else u x) *\<^sub>R x) = (\<Sum>x\<in>S. u x *\<^sub>R x)"
    using \<open>a \<notin> S\<close>
    by (auto simp: intro!: sum.cong)
  ultimately show ?lhs
    by (rule_tac x="\<lambda>x. if a = x then v else u x" in exI) (auto simp: sum_clauses(2)[OF assms])
qed


subsubsection\<^marker>\<open>tag unimportant\<close> \<open>Hence some special cases\<close>

lemma convex_hull_2:
  "convex hull {a,b} = {u *\<^sub>R a + v *\<^sub>R b | u v. 0 \<le> u \<and> 0 \<le> v \<and> u + v = 1}"
proof -
  have *: "\<And>u. (\<forall>x\<in>{a, b}. 0 \<le> u x) \<longleftrightarrow> 0 \<le> u a \<and> 0 \<le> u b"
    by auto
  have **: "finite {b}" by auto
  show ?thesis
    apply (simp add: convex_hull_finite)
    unfolding convex_hull_finite_step[OF **, of a 1, unfolded * conj_assoc]
    apply auto
    apply (rule_tac x=v in exI)
    apply (rule_tac x="1 - v" in exI, simp)
    apply (rule_tac x=u in exI, simp)
    apply (rule_tac x="\<lambda>x. v" in exI, simp)
    done
qed

lemma convex_hull_2_alt: "convex hull {a,b} = {a + u *\<^sub>R (b - a) | u.  0 \<le> u \<and> u \<le> 1}"
  unfolding convex_hull_2
proof (rule Collect_cong)
  have *: "\<And>x y ::real. x + y = 1 \<longleftrightarrow> x = 1 - y"
    by auto
  fix x
  show "(\<exists>v u. x = v *\<^sub>R a + u *\<^sub>R b \<and> 0 \<le> v \<and> 0 \<le> u \<and> v + u = 1) \<longleftrightarrow>
    (\<exists>u. x = a + u *\<^sub>R (b - a) \<and> 0 \<le> u \<and> u \<le> 1)"
    unfolding *
    apply auto
    apply (rule_tac[!] x=u in exI)
    apply (auto simp: algebra_simps)
    done
qed

lemma convex_hull_3:
  "convex hull {a,b,c} = { u *\<^sub>R a + v *\<^sub>R b + w *\<^sub>R c | u v w. 0 \<le> u \<and> 0 \<le> v \<and> 0 \<le> w \<and> u + v + w = 1}"
proof -
  have fin: "finite {a,b,c}" "finite {b,c}" "finite {c}"
    by auto
  have *: "\<And>x y z ::real. x + y + z = 1 \<longleftrightarrow> x = 1 - y - z"
    by (auto simp: field_simps)
  show ?thesis
    unfolding convex_hull_finite[OF fin(1)] and convex_hull_finite_step[OF fin(2)] and *
    unfolding convex_hull_finite_step[OF fin(3)]
    apply (rule Collect_cong, simp)
    apply auto
    apply (rule_tac x=va in exI)
    apply (rule_tac x="u c" in exI, simp)
    apply (rule_tac x="1 - v - w" in exI, simp)
    apply (rule_tac x=v in exI, simp)
    apply (rule_tac x="\<lambda>x. w" in exI, simp)
    done
qed

lemma convex_hull_3_alt:
  "convex hull {a,b,c} = {a + u *\<^sub>R (b - a) + v *\<^sub>R (c - a) | u v.  0 \<le> u \<and> 0 \<le> v \<and> u + v \<le> 1}"
proof -
  have *: "\<And>x y z ::real. x + y + z = 1 \<longleftrightarrow> x = 1 - y - z"
    by auto
  show ?thesis
    unfolding convex_hull_3
    apply (auto simp: *)
    apply (rule_tac x=v in exI)
    apply (rule_tac x=w in exI)
    apply (simp add: algebra_simps)
    apply (rule_tac x=u in exI)
    apply (rule_tac x=v in exI)
    apply (simp add: algebra_simps)
    done
qed


subsection\<^marker>\<open>tag unimportant\<close> \<open>Relations among closure notions and corresponding hulls\<close>

lemma affine_imp_convex: "affine s \<Longrightarrow> convex s"
  unfolding affine_def convex_def by auto

lemma convex_affine_hull [simp]: "convex (affine hull S)"
  by (simp add: affine_imp_convex)

lemma subspace_imp_convex: "subspace s \<Longrightarrow> convex s"
  using subspace_imp_affine affine_imp_convex by auto

lemma convex_hull_subset_span: "(convex hull s) \<subseteq> (span s)"
  by (metis hull_minimal span_superset subspace_imp_convex subspace_span)

lemma convex_hull_subset_affine_hull: "(convex hull s) \<subseteq> (affine hull s)"
  by (metis affine_affine_hull affine_imp_convex hull_minimal hull_subset)

lemma aff_dim_convex_hull:
  fixes S :: "'n::euclidean_space set"
  shows "aff_dim (convex hull S) = aff_dim S"
  using aff_dim_affine_hull[of S] convex_hull_subset_affine_hull[of S]
    hull_subset[of S "convex"] aff_dim_subset[of S "convex hull S"]
    aff_dim_subset[of "convex hull S" "affine hull S"]
  by auto


subsection \<open>Caratheodory's theorem\<close>

lemma convex_hull_caratheodory_aff_dim:
  fixes p :: "('a::euclidean_space) set"
  shows "convex hull p =
    {y. \<exists>s u. finite s \<and> s \<subseteq> p \<and> card s \<le> aff_dim p + 1 \<and>
      (\<forall>x\<in>s. 0 \<le> u x) \<and> sum u s = 1 \<and> sum (\<lambda>v. u v *\<^sub>R v) s = y}"
  unfolding convex_hull_explicit set_eq_iff mem_Collect_eq
proof (intro allI iffI)
  fix y
  let ?P = "\<lambda>n. \<exists>s u. finite s \<and> card s = n \<and> s \<subseteq> p \<and> (\<forall>x\<in>s. 0 \<le> u x) \<and>
    sum u s = 1 \<and> (\<Sum>v\<in>s. u v *\<^sub>R v) = y"
  assume "\<exists>s u. finite s \<and> s \<subseteq> p \<and> (\<forall>x\<in>s. 0 \<le> u x) \<and> sum u s = 1 \<and> (\<Sum>v\<in>s. u v *\<^sub>R v) = y"
  then obtain N where "?P N" by auto
  then have "\<exists>n\<le>N. (\<forall>k<n. \<not> ?P k) \<and> ?P n"
    apply (rule_tac ex_least_nat_le, auto)
    done
  then obtain n where "?P n" and smallest: "\<forall>k<n. \<not> ?P k"
    by blast
  then obtain s u where obt: "finite s" "card s = n" "s\<subseteq>p" "\<forall>x\<in>s. 0 \<le> u x"
    "sum u s = 1"  "(\<Sum>v\<in>s. u v *\<^sub>R v) = y" by auto

  have "card s \<le> aff_dim p + 1"
  proof (rule ccontr, simp only: not_le)
    assume "aff_dim p + 1 < card s"
    then have "affine_dependent s"
      using affine_dependent_biggerset[OF obt(1)] independent_card_le_aff_dim not_less obt(3)
      by blast
    then obtain w v where wv: "sum w s = 0" "v\<in>s" "w v \<noteq> 0" "(\<Sum>v\<in>s. w v *\<^sub>R v) = 0"
      using affine_dependent_explicit_finite[OF obt(1)] by auto
    define i where "i = (\<lambda>v. (u v) / (- w v)) ` {v\<in>s. w v < 0}"
    define t where "t = Min i"
    have "\<exists>x\<in>s. w x < 0"
    proof (rule ccontr, simp add: not_less)
      assume as:"\<forall>x\<in>s. 0 \<le> w x"
      then have "sum w (s - {v}) \<ge> 0"
        apply (rule_tac sum_nonneg, auto)
        done
      then have "sum w s > 0"
        unfolding sum.remove[OF obt(1) \<open>v\<in>s\<close>]
        using as[THEN bspec[where x=v]]  \<open>v\<in>s\<close>  \<open>w v \<noteq> 0\<close> by auto
      then show False using wv(1) by auto
    qed
    then have "i \<noteq> {}" unfolding i_def by auto
    then have "t \<ge> 0"
      using Min_ge_iff[of i 0 ] and obt(1)
      unfolding t_def i_def
      using obt(4)[unfolded le_less]
      by (auto simp: divide_le_0_iff)
    have t: "\<forall>v\<in>s. u v + t * w v \<ge> 0"
    proof
      fix v
      assume "v \<in> s"
      then have v: "0 \<le> u v"
        using obt(4)[THEN bspec[where x=v]] by auto
      show "0 \<le> u v + t * w v"
      proof (cases "w v < 0")
        case False
        thus ?thesis using v \<open>t\<ge>0\<close> by auto
      next
        case True
        then have "t \<le> u v / (- w v)"
          using \<open>v\<in>s\<close> unfolding t_def i_def
          apply (rule_tac Min_le)
          using obt(1) apply auto
          done
        then show ?thesis
          unfolding real_0_le_add_iff
          using pos_le_divide_eq[OF True[unfolded neg_0_less_iff_less[symmetric]]]
          by auto
      qed
    qed
    obtain a where "a \<in> s" and "t = (\<lambda>v. (u v) / (- w v)) a" and "w a < 0"
      using Min_in[OF _ \<open>i\<noteq>{}\<close>] and obt(1) unfolding i_def t_def by auto
    then have a: "a \<in> s" "u a + t * w a = 0" by auto
    have *: "\<And>f. sum f (s - {a}) = sum f s - ((f a)::'b::ab_group_add)"
      unfolding sum.remove[OF obt(1) \<open>a\<in>s\<close>] by auto
    have "(\<Sum>v\<in>s. u v + t * w v) = 1"
      unfolding sum.distrib wv(1) sum_distrib_left[symmetric] obt(5) by auto
    moreover have "(\<Sum>v\<in>s. u v *\<^sub>R v + (t * w v) *\<^sub>R v) - (u a *\<^sub>R a + (t * w a) *\<^sub>R a) = y"
      unfolding sum.distrib obt(6) scaleR_scaleR[symmetric] scaleR_right.sum [symmetric] wv(4)
      using a(2) [THEN eq_neg_iff_add_eq_0 [THEN iffD2]] by simp
    ultimately have "?P (n - 1)"
      apply (rule_tac x="(s - {a})" in exI)
      apply (rule_tac x="\<lambda>v. u v + t * w v" in exI)
      using obt(1-3) and t and a
      apply (auto simp: * scaleR_left_distrib)
      done
    then show False
      using smallest[THEN spec[where x="n - 1"]] by auto
  qed
  then show "\<exists>s u. finite s \<and> s \<subseteq> p \<and> card s \<le> aff_dim p + 1 \<and>
      (\<forall>x\<in>s. 0 \<le> u x) \<and> sum u s = 1 \<and> (\<Sum>v\<in>s. u v *\<^sub>R v) = y"
    using obt by auto
qed auto

lemma caratheodory_aff_dim:
  fixes p :: "('a::euclidean_space) set"
  shows "convex hull p = {x. \<exists>s. finite s \<and> s \<subseteq> p \<and> card s \<le> aff_dim p + 1 \<and> x \<in> convex hull s}"
        (is "?lhs = ?rhs")
proof
  show "?lhs \<subseteq> ?rhs"
    apply (subst convex_hull_caratheodory_aff_dim, clarify)
    apply (rule_tac x=s in exI)
    apply (simp add: hull_subset convex_explicit [THEN iffD1, OF convex_convex_hull])
    done
next
  show "?rhs \<subseteq> ?lhs"
    using hull_mono by blast
qed

lemma convex_hull_caratheodory:
  fixes p :: "('a::euclidean_space) set"
  shows "convex hull p =
            {y. \<exists>s u. finite s \<and> s \<subseteq> p \<and> card s \<le> DIM('a) + 1 \<and>
              (\<forall>x\<in>s. 0 \<le> u x) \<and> sum u s = 1 \<and> sum (\<lambda>v. u v *\<^sub>R v) s = y}"
        (is "?lhs = ?rhs")
proof (intro set_eqI iffI)
  fix x
  assume "x \<in> ?lhs" then show "x \<in> ?rhs"
    apply (simp only: convex_hull_caratheodory_aff_dim Set.mem_Collect_eq)
    apply (erule ex_forward)+
    using aff_dim_le_DIM [of p]
    apply simp
    done
next
  fix x
  assume "x \<in> ?rhs" then show "x \<in> ?lhs"
    by (auto simp: convex_hull_explicit)
qed

theorem caratheodory:
  "convex hull p =
    {x::'a::euclidean_space. \<exists>s. finite s \<and> s \<subseteq> p \<and>
      card s \<le> DIM('a) + 1 \<and> x \<in> convex hull s}"
proof safe
  fix x
  assume "x \<in> convex hull p"
  then obtain s u where "finite s" "s \<subseteq> p" "card s \<le> DIM('a) + 1"
    "\<forall>x\<in>s. 0 \<le> u x" "sum u s = 1" "(\<Sum>v\<in>s. u v *\<^sub>R v) = x"
    unfolding convex_hull_caratheodory by auto
  then show "\<exists>s. finite s \<and> s \<subseteq> p \<and> card s \<le> DIM('a) + 1 \<and> x \<in> convex hull s"
    apply (rule_tac x=s in exI)
    using hull_subset[of s convex]
    using convex_convex_hull[simplified convex_explicit, of s,
      THEN spec[where x=s], THEN spec[where x=u]]
    apply auto
    done
next
  fix x s
  assume  "finite s" "s \<subseteq> p" "card s \<le> DIM('a) + 1" "x \<in> convex hull s"
  then show "x \<in> convex hull p"
    using hull_mono[OF \<open>s\<subseteq>p\<close>] by auto
qed

subsection\<^marker>\<open>tag unimportant\<close>\<open>Some Properties of subset of standard basis\<close>

lemma affine_hull_substd_basis:
  assumes "d \<subseteq> Basis"
  shows "affine hull (insert 0 d) = {x::'a::euclidean_space. \<forall>i\<in>Basis. i \<notin> d \<longrightarrow> x\<bullet>i = 0}"
  (is "affine hull (insert 0 ?A) = ?B")
proof -
  have *: "\<And>A. (+) (0::'a) ` A = A" "\<And>A. (+) (- (0::'a)) ` A = A"
    by auto
  show ?thesis
    unfolding affine_hull_insert_span_gen span_substd_basis[OF assms,symmetric] * ..
qed

lemma affine_hull_convex_hull [simp]: "affine hull (convex hull S) = affine hull S"
  by (metis Int_absorb1 Int_absorb2 convex_hull_subset_affine_hull hull_hull hull_mono hull_subset)


subsection\<^marker>\<open>tag unimportant\<close> \<open>Moving and scaling convex hulls\<close>

lemma convex_hull_set_plus:
  "convex hull (S + T) = convex hull S + convex hull T"
  unfolding set_plus_image
  apply (subst convex_hull_linear_image [symmetric])
  apply (simp add: linear_iff scaleR_right_distrib)
  apply (simp add: convex_hull_Times)
  done

lemma translation_eq_singleton_plus: "(\<lambda>x. a + x) ` T = {a} + T"
  unfolding set_plus_def by auto

lemma convex_hull_translation:
  "convex hull ((\<lambda>x. a + x) ` S) = (\<lambda>x. a + x) ` (convex hull S)"
  unfolding translation_eq_singleton_plus
  by (simp only: convex_hull_set_plus convex_hull_singleton)

lemma convex_hull_scaling:
  "convex hull ((\<lambda>x. c *\<^sub>R x) ` S) = (\<lambda>x. c *\<^sub>R x) ` (convex hull S)"
  using linear_scaleR by (rule convex_hull_linear_image [symmetric])

lemma convex_hull_affinity:
  "convex hull ((\<lambda>x. a + c *\<^sub>R x) ` S) = (\<lambda>x. a + c *\<^sub>R x) ` (convex hull S)"
  by(simp only: image_image[symmetric] convex_hull_scaling convex_hull_translation)


subsection\<^marker>\<open>tag unimportant\<close> \<open>Convexity of cone hulls\<close>

lemma convex_cone_hull:
  assumes "convex S"
  shows "convex (cone hull S)"
proof (rule convexI)
  fix x y
  assume xy: "x \<in> cone hull S" "y \<in> cone hull S"
  then have "S \<noteq> {}"
    using cone_hull_empty_iff[of S] by auto
  fix u v :: real
  assume uv: "u \<ge> 0" "v \<ge> 0" "u + v = 1"
  then have *: "u *\<^sub>R x \<in> cone hull S" "v *\<^sub>R y \<in> cone hull S"
    using cone_cone_hull[of S] xy cone_def[of "cone hull S"] by auto
  from * obtain cx :: real and xx where x: "u *\<^sub>R x = cx *\<^sub>R xx" "cx \<ge> 0" "xx \<in> S"
    using cone_hull_expl[of S] by auto
  from * obtain cy :: real and yy where y: "v *\<^sub>R y = cy *\<^sub>R yy" "cy \<ge> 0" "yy \<in> S"
    using cone_hull_expl[of S] by auto
  {
    assume "cx + cy \<le> 0"
    then have "u *\<^sub>R x = 0" and "v *\<^sub>R y = 0"
      using x y by auto
    then have "u *\<^sub>R x + v *\<^sub>R y = 0"
      by auto
    then have "u *\<^sub>R x + v *\<^sub>R y \<in> cone hull S"
      using cone_hull_contains_0[of S] \<open>S \<noteq> {}\<close> by auto
  }
  moreover
  {
    assume "cx + cy > 0"
    then have "(cx / (cx + cy)) *\<^sub>R xx + (cy / (cx + cy)) *\<^sub>R yy \<in> S"
      using assms mem_convex_alt[of S xx yy cx cy] x y by auto
    then have "cx *\<^sub>R xx + cy *\<^sub>R yy \<in> cone hull S"
      using mem_cone_hull[of "(cx/(cx+cy)) *\<^sub>R xx + (cy/(cx+cy)) *\<^sub>R yy" S "cx+cy"] \<open>cx+cy>0\<close>
      by (auto simp: scaleR_right_distrib)
    then have "u *\<^sub>R x + v *\<^sub>R y \<in> cone hull S"
      using x y by auto
  }
  moreover have "cx + cy \<le> 0 \<or> cx + cy > 0" by auto
  ultimately show "u *\<^sub>R x + v *\<^sub>R y \<in> cone hull S" by blast
qed

lemma cone_convex_hull:
  assumes "cone S"
  shows "cone (convex hull S)"
proof (cases "S = {}")
  case True
  then show ?thesis by auto
next
  case False
  then have *: "0 \<in> S \<and> (\<forall>c. c > 0 \<longrightarrow> (*\<^sub>R) c ` S = S)"
    using cone_iff[of S] assms by auto
  {
    fix c :: real
    assume "c > 0"
    then have "(*\<^sub>R) c ` (convex hull S) = convex hull ((*\<^sub>R) c ` S)"
      using convex_hull_scaling[of _ S] by auto
    also have "\<dots> = convex hull S"
      using * \<open>c > 0\<close> by auto
    finally have "(*\<^sub>R) c ` (convex hull S) = convex hull S"
      by auto
  }
  then have "0 \<in> convex hull S" "\<And>c. c > 0 \<Longrightarrow> ((*\<^sub>R) c ` (convex hull S)) = (convex hull S)"
    using * hull_subset[of S convex] by auto
  then show ?thesis
    using \<open>S \<noteq> {}\<close> cone_iff[of "convex hull S"] by auto
qed

subsection \<open>Radon's theorem\<close>

text "Formalized by Lars Schewe."

lemma Radon_ex_lemma:
  assumes "finite c" "affine_dependent c"
  shows "\<exists>u. sum u c = 0 \<and> (\<exists>v\<in>c. u v \<noteq> 0) \<and> sum (\<lambda>v. u v *\<^sub>R v) c = 0"
proof -
  from assms(2)[unfolded affine_dependent_explicit]
  obtain s u where
      "finite s" "s \<subseteq> c" "sum u s = 0" "\<exists>v\<in>s. u v \<noteq> 0" "(\<Sum>v\<in>s. u v *\<^sub>R v) = 0"
    by blast
  then show ?thesis
    apply (rule_tac x="\<lambda>v. if v\<in>s then u v else 0" in exI)
    unfolding if_smult scaleR_zero_left and sum.inter_restrict[OF assms(1), symmetric]
    apply (auto simp: Int_absorb1)
    done
qed

lemma Radon_s_lemma:
  assumes "finite s"
    and "sum f s = (0::real)"
  shows "sum f {x\<in>s. 0 < f x} = - sum f {x\<in>s. f x < 0}"
proof -
  have *: "\<And>x. (if f x < 0 then f x else 0) + (if 0 < f x then f x else 0) = f x"
    by auto
  show ?thesis
    unfolding add_eq_0_iff[symmetric] and sum.inter_filter[OF assms(1)]
      and sum.distrib[symmetric] and *
    using assms(2)
    by assumption
qed

lemma Radon_v_lemma:
  assumes "finite s"
    and "sum f s = 0"
    and "\<forall>x. g x = (0::real) \<longrightarrow> f x = (0::'a::euclidean_space)"
  shows "(sum f {x\<in>s. 0 < g x}) = - sum f {x\<in>s. g x < 0}"
proof -
  have *: "\<And>x. (if 0 < g x then f x else 0) + (if g x < 0 then f x else 0) = f x"
    using assms(3) by auto
  show ?thesis
    unfolding eq_neg_iff_add_eq_0 and sum.inter_filter[OF assms(1)]
      and sum.distrib[symmetric] and *
    using assms(2)
    apply assumption
    done
qed

lemma Radon_partition:
  assumes "finite c" "affine_dependent c"
  shows "\<exists>m p. m \<inter> p = {} \<and> m \<union> p = c \<and> (convex hull m) \<inter> (convex hull p) \<noteq> {}"
proof -
  obtain u v where uv: "sum u c = 0" "v\<in>c" "u v \<noteq> 0"  "(\<Sum>v\<in>c. u v *\<^sub>R v) = 0"
    using Radon_ex_lemma[OF assms] by auto
  have fin: "finite {x \<in> c. 0 < u x}" "finite {x \<in> c. 0 > u x}"
    using assms(1) by auto
  define z  where "z = inverse (sum u {x\<in>c. u x > 0}) *\<^sub>R sum (\<lambda>x. u x *\<^sub>R x) {x\<in>c. u x > 0}"
  have "sum u {x \<in> c. 0 < u x} \<noteq> 0"
  proof (cases "u v \<ge> 0")
    case False
    then have "u v < 0" by auto
    then show ?thesis
    proof (cases "\<exists>w\<in>{x \<in> c. 0 < u x}. u w > 0")
      case True
      then show ?thesis
        using sum_nonneg_eq_0_iff[of _ u, OF fin(1)] by auto
    next
      case False
      then have "sum u c \<le> sum (\<lambda>x. if x=v then u v else 0) c"
        apply (rule_tac sum_mono, auto)
        done
      then show ?thesis
        unfolding sum.delta[OF assms(1)] using uv(2) and \<open>u v < 0\<close> and uv(1) by auto
    qed
  qed (insert sum_nonneg_eq_0_iff[of _ u, OF fin(1)] uv(2-3), auto)

  then have *: "sum u {x\<in>c. u x > 0} > 0"
    unfolding less_le
    apply (rule_tac conjI)
    apply (rule_tac sum_nonneg, auto)
    done
  moreover have "sum u ({x \<in> c. 0 < u x} \<union> {x \<in> c. u x < 0}) = sum u c"
    "(\<Sum>x\<in>{x \<in> c. 0 < u x} \<union> {x \<in> c. u x < 0}. u x *\<^sub>R x) = (\<Sum>x\<in>c. u x *\<^sub>R x)"
    using assms(1)
    apply (rule_tac[!] sum.mono_neutral_left, auto)
    done
  then have "sum u {x \<in> c. 0 < u x} = - sum u {x \<in> c. 0 > u x}"
    "(\<Sum>x\<in>{x \<in> c. 0 < u x}. u x *\<^sub>R x) = - (\<Sum>x\<in>{x \<in> c. 0 > u x}. u x *\<^sub>R x)"
    unfolding eq_neg_iff_add_eq_0
    using uv(1,4)
    by (auto simp: sum.union_inter_neutral[OF fin, symmetric])
  moreover have "\<forall>x\<in>{v \<in> c. u v < 0}. 0 \<le> inverse (sum u {x \<in> c. 0 < u x}) * - u x"
    apply rule
    apply (rule mult_nonneg_nonneg)
    using *
    apply auto
    done
  ultimately have "z \<in> convex hull {v \<in> c. u v \<le> 0}"
    unfolding convex_hull_explicit mem_Collect_eq
    apply (rule_tac x="{v \<in> c. u v < 0}" in exI)
    apply (rule_tac x="\<lambda>y. inverse (sum u {x\<in>c. u x > 0}) * - u y" in exI)
    using assms(1) unfolding scaleR_scaleR[symmetric] scaleR_right.sum [symmetric] and z_def
    apply (auto simp: sum_negf sum_distrib_left[symmetric])
    done
  moreover have "\<forall>x\<in>{v \<in> c. 0 < u v}. 0 \<le> inverse (sum u {x \<in> c. 0 < u x}) * u x"
    apply rule
    apply (rule mult_nonneg_nonneg)
    using *
    apply auto
    done
  then have "z \<in> convex hull {v \<in> c. u v > 0}"
    unfolding convex_hull_explicit mem_Collect_eq
    apply (rule_tac x="{v \<in> c. 0 < u v}" in exI)
    apply (rule_tac x="\<lambda>y. inverse (sum u {x\<in>c. u x > 0}) * u y" in exI)
    using assms(1)
    unfolding scaleR_scaleR[symmetric] scaleR_right.sum [symmetric] and z_def
    using *
    apply (auto simp: sum_negf sum_distrib_left[symmetric])
    done
  ultimately show ?thesis
    apply (rule_tac x="{v\<in>c. u v \<le> 0}" in exI)
    apply (rule_tac x="{v\<in>c. u v > 0}" in exI, auto)
    done
qed

theorem Radon:
  assumes "affine_dependent c"
  obtains m p where "m \<subseteq> c" "p \<subseteq> c" "m \<inter> p = {}" "(convex hull m) \<inter> (convex hull p) \<noteq> {}"
proof -
  from assms[unfolded affine_dependent_explicit]
  obtain s u where
      "finite s" "s \<subseteq> c" "sum u s = 0" "\<exists>v\<in>s. u v \<noteq> 0" "(\<Sum>v\<in>s. u v *\<^sub>R v) = 0"
    by blast
  then have *: "finite s" "affine_dependent s" and s: "s \<subseteq> c"
    unfolding affine_dependent_explicit by auto
  from Radon_partition[OF *]
  obtain m p where "m \<inter> p = {}" "m \<union> p = s" "convex hull m \<inter> convex hull p \<noteq> {}"
    by blast
  then show ?thesis
    apply (rule_tac that[of p m])
    using s
    apply auto
    done
qed


subsection \<open>Helly's theorem\<close>

lemma Helly_induct:
  fixes f :: "'a::euclidean_space set set"
  assumes "card f = n"
    and "n \<ge> DIM('a) + 1"
    and "\<forall>s\<in>f. convex s" "\<forall>t\<subseteq>f. card t = DIM('a) + 1 \<longrightarrow> \<Inter>t \<noteq> {}"
  shows "\<Inter>f \<noteq> {}"
  using assms
proof (induction n arbitrary: f)
  case 0
  then show ?case by auto
next
  case (Suc n)
  have "finite f"
    using \<open>card f = Suc n\<close> by (auto intro: card_ge_0_finite)
  show "\<Inter>f \<noteq> {}"
  proof (cases "n = DIM('a)")
    case True
    then show ?thesis
      by (simp add: Suc.prems(1) Suc.prems(4))
  next
    case False
    have "\<Inter>(f - {s}) \<noteq> {}" if "s \<in> f" for s
    proof (rule Suc.IH[rule_format])
      show "card (f - {s}) = n"
        by (simp add: Suc.prems(1) \<open>finite f\<close> that)
      show "DIM('a) + 1 \<le> n"
        using False Suc.prems(2) by linarith
      show "\<And>t. \<lbrakk>t \<subseteq> f - {s}; card t = DIM('a) + 1\<rbrakk> \<Longrightarrow> \<Inter>t \<noteq> {}"
        by (simp add: Suc.prems(4) subset_Diff_insert)
    qed (use Suc in auto)
    then have "\<forall>s\<in>f. \<exists>x. x \<in> \<Inter>(f - {s})"
      by blast
    then obtain X where X: "\<And>s. s\<in>f \<Longrightarrow> X s \<in> \<Inter>(f - {s})"
      by metis
    show ?thesis
    proof (cases "inj_on X f")
      case False
      then obtain s t where "s\<noteq>t" and st: "s\<in>f" "t\<in>f" "X s = X t"
        unfolding inj_on_def by auto
      then have *: "\<Inter>f = \<Inter>(f - {s}) \<inter> \<Inter>(f - {t})" by auto
      show ?thesis
        by (metis "*" X disjoint_iff_not_equal st)
    next
      case True
      then obtain m p where mp: "m \<inter> p = {}" "m \<union> p = X ` f" "convex hull m \<inter> convex hull p \<noteq> {}"
        using Radon_partition[of "X ` f"] and affine_dependent_biggerset[of "X ` f"]
        unfolding card_image[OF True] and \<open>card f = Suc n\<close>
        using Suc(3) \<open>finite f\<close> and False
        by auto
      have "m \<subseteq> X ` f" "p \<subseteq> X ` f"
        using mp(2) by auto
      then obtain g h where gh:"m = X ` g" "p = X ` h" "g \<subseteq> f" "h \<subseteq> f"
        unfolding subset_image_iff by auto
      then have "f \<union> (g \<union> h) = f" by auto
      then have f: "f = g \<union> h"
        using inj_on_Un_image_eq_iff[of X f "g \<union> h"] and True
        unfolding mp(2)[unfolded image_Un[symmetric] gh]
        by auto
      have *: "g \<inter> h = {}"
        using mp(1)
        unfolding gh
        using inj_on_image_Int[OF True gh(3,4)]
        by auto
      have "convex hull (X ` h) \<subseteq> \<Inter>g" "convex hull (X ` g) \<subseteq> \<Inter>h"
        by (rule hull_minimal; use X * f in \<open>auto simp: Suc.prems(3) convex_Inter\<close>)+
      then show ?thesis
        unfolding f using mp(3)[unfolded gh] by blast
    qed
  qed 
qed

theorem Helly:
  fixes f :: "'a::euclidean_space set set"
  assumes "card f \<ge> DIM('a) + 1" "\<forall>s\<in>f. convex s"
    and "\<And>t. \<lbrakk>t\<subseteq>f; card t = DIM('a) + 1\<rbrakk> \<Longrightarrow> \<Inter>t \<noteq> {}"
  shows "\<Inter>f \<noteq> {}"
  apply (rule Helly_induct)
  using assms
  apply auto
  done

subsection \<open>Epigraphs of convex functions\<close>

definition\<^marker>\<open>tag important\<close> "epigraph S (f :: _ \<Rightarrow> real) = {xy. fst xy \<in> S \<and> f (fst xy) \<le> snd xy}"

lemma mem_epigraph: "(x, y) \<in> epigraph S f \<longleftrightarrow> x \<in> S \<and> f x \<le> y"
  unfolding epigraph_def by auto

lemma convex_epigraph: "convex (epigraph S f) \<longleftrightarrow> convex_on S f \<and> convex S"
proof safe
  assume L: "convex (epigraph S f)"
  then show "convex_on S f"
    by (auto simp: convex_def convex_on_def epigraph_def)
  show "convex S"
    using L
    apply (clarsimp simp: convex_def convex_on_def epigraph_def)
    apply (erule_tac x=x in allE)
    apply (erule_tac x="f x" in allE, safe)
    apply (erule_tac x=y in allE)
    apply (erule_tac x="f y" in allE)
    apply (auto simp: )
    done
next
  assume "convex_on S f" "convex S"
  then show "convex (epigraph S f)"
    unfolding convex_def convex_on_def epigraph_def
    apply safe
     apply (rule_tac [2] y="u * f a + v * f aa" in order_trans)
      apply (auto intro!:mult_left_mono add_mono)
    done
qed

lemma convex_epigraphI: "convex_on S f \<Longrightarrow> convex S \<Longrightarrow> convex (epigraph S f)"
  unfolding convex_epigraph by auto

lemma convex_epigraph_convex: "convex S \<Longrightarrow> convex_on S f \<longleftrightarrow> convex(epigraph S f)"
  by (simp add: convex_epigraph)


subsubsection\<^marker>\<open>tag unimportant\<close> \<open>Use this to derive general bound property of convex function\<close>

lemma convex_on:
  assumes "convex S"
  shows "convex_on S f \<longleftrightarrow>
    (\<forall>k u x. (\<forall>i\<in>{1..k::nat}. 0 \<le> u i \<and> x i \<in> S) \<and> sum u {1..k} = 1 \<longrightarrow>
      f (sum (\<lambda>i. u i *\<^sub>R x i) {1..k}) \<le> sum (\<lambda>i. u i * f(x i)) {1..k})"

  unfolding convex_epigraph_convex[OF assms] convex epigraph_def Ball_def mem_Collect_eq
  unfolding fst_sum snd_sum fst_scaleR snd_scaleR
  apply safe
    apply (drule_tac x=k in spec)
    apply (drule_tac x=u in spec)
    apply (drule_tac x="\<lambda>i. (x i, f (x i))" in spec)
    apply simp
  using assms[unfolded convex] apply simp
  apply (rule_tac y="\<Sum>i = 1..k. u i * f (fst (x i))" in order_trans, force)
   apply (rule sum_mono)
   apply (erule_tac x=i in allE)
  unfolding real_scaleR_def
   apply (rule mult_left_mono)
  using assms[unfolded convex] apply auto
  done

subsection\<^marker>\<open>tag unimportant\<close> \<open>A bound within a convex hull\<close>

lemma convex_on_convex_hull_bound:
  assumes "convex_on (convex hull s) f"
    and "\<forall>x\<in>s. f x \<le> b"
  shows "\<forall>x\<in> convex hull s. f x \<le> b"
proof
  fix x
  assume "x \<in> convex hull s"
  then obtain k u v where
    obt: "\<forall>i\<in>{1..k::nat}. 0 \<le> u i \<and> v i \<in> s" "sum u {1..k} = 1" "(\<Sum>i = 1..k. u i *\<^sub>R v i) = x"
    unfolding convex_hull_indexed mem_Collect_eq by auto
  have "(\<Sum>i = 1..k. u i * f (v i)) \<le> b"
    using sum_mono[of "{1..k}" "\<lambda>i. u i * f (v i)" "\<lambda>i. u i * b"]
    unfolding sum_distrib_right[symmetric] obt(2) mult_1
    apply (drule_tac meta_mp)
    apply (rule mult_left_mono)
    using assms(2) obt(1)
    apply auto
    done
  then show "f x \<le> b"
    using assms(1)[unfolded convex_on[OF convex_convex_hull], rule_format, of k u v]
    unfolding obt(2-3)
    using obt(1) and hull_subset[unfolded subset_eq, rule_format, of _ s]
    by auto
qed

lemma inner_sum_Basis[simp]: "i \<in> Basis \<Longrightarrow> (\<Sum>Basis) \<bullet> i = 1"
  by (simp add: inner_sum_left sum.If_cases inner_Basis)

lemma convex_set_plus:
  assumes "convex S" and "convex T" shows "convex (S + T)"
proof -
  have "convex (\<Union>x\<in> S. \<Union>y \<in> T. {x + y})"
    using assms by (rule convex_sums)
  moreover have "(\<Union>x\<in> S. \<Union>y \<in> T. {x + y}) = S + T"
    unfolding set_plus_def by auto
  finally show "convex (S + T)" .
qed

lemma convex_set_sum:
  assumes "\<And>i. i \<in> A \<Longrightarrow> convex (B i)"
  shows "convex (\<Sum>i\<in>A. B i)"
proof (cases "finite A")
  case True then show ?thesis using assms
    by induct (auto simp: convex_set_plus)
qed auto

lemma finite_set_sum:
  assumes "finite A" and "\<forall>i\<in>A. finite (B i)" shows "finite (\<Sum>i\<in>A. B i)"
  using assms by (induct set: finite, simp, simp add: finite_set_plus)

lemma box_eq_set_sum_Basis:
  shows "{x. \<forall>i\<in>Basis. x\<bullet>i \<in> B i} = (\<Sum>i\<in>Basis. image (\<lambda>x. x *\<^sub>R i) (B i))"
  apply (subst set_sum_alt [OF finite_Basis], safe)
  apply (fast intro: euclidean_representation [symmetric])
  apply (subst inner_sum_left)
apply (rename_tac f)
  apply (subgoal_tac "(\<Sum>x\<in>Basis. f x \<bullet> i) = f i \<bullet> i")
  apply (drule (1) bspec)
  apply clarsimp
  apply (frule sum.remove [OF finite_Basis])
  apply (erule trans, simp)
  apply (rule sum.neutral, clarsimp)
  apply (frule_tac x=i in bspec, assumption)
  apply (drule_tac x=x in bspec, assumption, clarsimp)
  apply (cut_tac u=x and v=i in inner_Basis, assumption+)
  apply (rule ccontr, simp)
  done

lemma convex_hull_set_sum:
  "convex hull (\<Sum>i\<in>A. B i) = (\<Sum>i\<in>A. convex hull (B i))"
proof (cases "finite A")
  assume "finite A" then show ?thesis
    by (induct set: finite, simp, simp add: convex_hull_set_plus)
qed simp


end