Theory Product_Vector

theory Product_Vector
imports Inner_Product Product_Plus
(*  Title:      HOL/Analysis/Product_Vector.thy
    Author:     Brian Huffman

section ‹Cartesian Products as Vector Spaces›

theory Product_Vector

subsection ‹Product is a real vector space›

instantiation prod :: (real_vector, real_vector) real_vector

definition scaleR_prod_def:
  "scaleR r A = (scaleR r (fst A), scaleR r (snd A))"

lemma fst_scaleR [simp]: "fst (scaleR r A) = scaleR r (fst A)"
  unfolding scaleR_prod_def by simp

lemma snd_scaleR [simp]: "snd (scaleR r A) = scaleR r (snd A)"
  unfolding scaleR_prod_def by simp

lemma scaleR_Pair [simp]: "scaleR r (a, b) = (scaleR r a, scaleR r b)"
  unfolding scaleR_prod_def by simp

  fix a b :: real and x y :: "'a × 'b"
  show "scaleR a (x + y) = scaleR a x + scaleR a y"
    by (simp add: prod_eq_iff scaleR_right_distrib)
  show "scaleR (a + b) x = scaleR a x + scaleR b x"
    by (simp add: prod_eq_iff scaleR_left_distrib)
  show "scaleR a (scaleR b x) = scaleR (a * b) x"
    by (simp add: prod_eq_iff)
  show "scaleR 1 x = x"
    by (simp add: prod_eq_iff)


subsection ‹Product is a metric space›

(* TODO: Product of uniform spaces and compatibility with metric_spaces! *)

instantiation prod :: (metric_space, metric_space) dist

definition dist_prod_def[code del]:
  "dist x y = sqrt ((dist (fst x) (fst y))2 + (dist (snd x) (snd y))2)"

instance ..

instantiation prod :: (metric_space, metric_space) uniformity_dist

definition [code del]:
  "(uniformity :: (('a × 'b) × ('a × 'b)) filter) =
    (INF e:{0 <..}. principal {(x, y). dist x y < e})"

  by standard (rule uniformity_prod_def)

declare uniformity_Abort[where 'a="'a :: metric_space × 'b :: metric_space", code]

instantiation prod :: (metric_space, metric_space) metric_space

lemma dist_Pair_Pair: "dist (a, b) (c, d) = sqrt ((dist a c)2 + (dist b d)2)"
  unfolding dist_prod_def by simp

lemma dist_fst_le: "dist (fst x) (fst y) ≤ dist x y"
  unfolding dist_prod_def by (rule real_sqrt_sum_squares_ge1)

lemma dist_snd_le: "dist (snd x) (snd y) ≤ dist x y"
  unfolding dist_prod_def by (rule real_sqrt_sum_squares_ge2)

  fix x y :: "'a × 'b"
  show "dist x y = 0 ⟷ x = y"
    unfolding dist_prod_def prod_eq_iff by simp
  fix x y z :: "'a × 'b"
  show "dist x y ≤ dist x z + dist y z"
    unfolding dist_prod_def
    by (intro order_trans [OF _ real_sqrt_sum_squares_triangle_ineq]
        real_sqrt_le_mono add_mono power_mono dist_triangle2 zero_le_dist)
  fix S :: "('a × 'b) set"
  have *: "open S ⟷ (∀x∈S. ∃e>0. ∀y. dist y x < e ⟶ y ∈ S)"
    assume "open S" show "∀x∈S. ∃e>0. ∀y. dist y x < e ⟶ y ∈ S"
      fix x assume "x ∈ S"
      obtain A B where "open A" "open B" "x ∈ A × B" "A × B ⊆ S"
        using ‹open S› and ‹x ∈ S› by (rule open_prod_elim)
      obtain r where r: "0 < r" "∀y. dist y (fst x) < r ⟶ y ∈ A"
        using ‹open A› and ‹x ∈ A × B› unfolding open_dist by auto
      obtain s where s: "0 < s" "∀y. dist y (snd x) < s ⟶ y ∈ B"
        using ‹open B› and ‹x ∈ A × B› unfolding open_dist by auto
      let ?e = "min r s"
      have "0 < ?e ∧ (∀y. dist y x < ?e ⟶ y ∈ S)"
      proof (intro allI impI conjI)
        show "0 < min r s" by (simp add: r(1) s(1))
        fix y assume "dist y x < min r s"
        hence "dist y x < r" and "dist y x < s"
          by simp_all
        hence "dist (fst y) (fst x) < r" and "dist (snd y) (snd x) < s"
          by (auto intro: le_less_trans dist_fst_le dist_snd_le)
        hence "fst y ∈ A" and "snd y ∈ B"
          by (simp_all add: r(2) s(2))
        hence "y ∈ A × B" by (induct y, simp)
        with ‹A × B ⊆ S› show "y ∈ S" ..
      thus "∃e>0. ∀y. dist y x < e ⟶ y ∈ S" ..
    assume *: "∀x∈S. ∃e>0. ∀y. dist y x < e ⟶ y ∈ S" show "open S"
    proof (rule open_prod_intro)
      fix x assume "x ∈ S"
      then obtain e where "0 < e" and S: "∀y. dist y x < e ⟶ y ∈ S"
        using * by fast
      define r where "r = e / sqrt 2"
      define s where "s = e / sqrt 2"
      from ‹0 < e› have "0 < r" and "0 < s"
        unfolding r_def s_def by simp_all
      from ‹0 < e› have "e = sqrt (r2 + s2)"
        unfolding r_def s_def by (simp add: power_divide)
      define A where "A = {y. dist (fst x) y < r}"
      define B where "B = {y. dist (snd x) y < s}"
      have "open A" and "open B"
        unfolding A_def B_def by (simp_all add: open_ball)
      moreover have "x ∈ A × B"
        unfolding A_def B_def mem_Times_iff
        using ‹0 < r› and ‹0 < s› by simp
      moreover have "A × B ⊆ S"
      proof (clarify)
        fix a b assume "a ∈ A" and "b ∈ B"
        hence "dist a (fst x) < r" and "dist b (snd x) < s"
          unfolding A_def B_def by (simp_all add: dist_commute)
        hence "dist (a, b) x < e"
          unfolding dist_prod_def ‹e = sqrt (r2 + s2)›
          by (simp add: add_strict_mono power_strict_mono)
        thus "(a, b) ∈ S"
          by (simp add: S)
      ultimately show "∃A B. open A ∧ open B ∧ x ∈ A × B ∧ A × B ⊆ S" by fast
  show "open S = (∀x∈S. ∀F (x', y) in uniformity. x' = x ⟶ y ∈ S)"
    unfolding * eventually_uniformity_metric
    by (simp del: split_paired_All add: dist_prod_def dist_commute)


declare [[code abort: "dist::('a::metric_space*'b::metric_space)⇒('a*'b) ⇒ real"]]

lemma Cauchy_fst: "Cauchy X ⟹ Cauchy (λn. fst (X n))"
  unfolding Cauchy_def by (fast elim: le_less_trans [OF dist_fst_le])

lemma Cauchy_snd: "Cauchy X ⟹ Cauchy (λn. snd (X n))"
  unfolding Cauchy_def by (fast elim: le_less_trans [OF dist_snd_le])

lemma Cauchy_Pair:
  assumes "Cauchy X" and "Cauchy Y"
  shows "Cauchy (λn. (X n, Y n))"
proof (rule metric_CauchyI)
  fix r :: real assume "0 < r"
  hence "0 < r / sqrt 2" (is "0 < ?s") by simp
  obtain M where M: "∀m≥M. ∀n≥M. dist (X m) (X n) < ?s"
    using metric_CauchyD [OF ‹Cauchy X› ‹0 < ?s›] ..
  obtain N where N: "∀m≥N. ∀n≥N. dist (Y m) (Y n) < ?s"
    using metric_CauchyD [OF ‹Cauchy Y› ‹0 < ?s›] ..
  have "∀m≥max M N. ∀n≥max M N. dist (X m, Y m) (X n, Y n) < r"
    using M N by (simp add: real_sqrt_sum_squares_less dist_Pair_Pair)
  then show "∃n0. ∀m≥n0. ∀n≥n0. dist (X m, Y m) (X n, Y n) < r" ..

subsection ‹Product is a complete metric space›

instance prod :: (complete_space, complete_space) complete_space
  fix X :: "nat ⇒ 'a × 'b" assume "Cauchy X"
  have 1: "(λn. fst (X n)) ⇢ lim (λn. fst (X n))"
    using Cauchy_fst [OF ‹Cauchy X›]
    by (simp add: Cauchy_convergent_iff convergent_LIMSEQ_iff)
  have 2: "(λn. snd (X n)) ⇢ lim (λn. snd (X n))"
    using Cauchy_snd [OF ‹Cauchy X›]
    by (simp add: Cauchy_convergent_iff convergent_LIMSEQ_iff)
  have "X ⇢ (lim (λn. fst (X n)), lim (λn. snd (X n)))"
    using tendsto_Pair [OF 1 2] by simp
  then show "convergent X"
    by (rule convergentI)

subsection ‹Product is a normed vector space›

instantiation prod :: (real_normed_vector, real_normed_vector) real_normed_vector

definition norm_prod_def[code del]:
  "norm x = sqrt ((norm (fst x))2 + (norm (snd x))2)"

definition sgn_prod_def:
  "sgn (x::'a × 'b) = scaleR (inverse (norm x)) x"

lemma norm_Pair: "norm (a, b) = sqrt ((norm a)2 + (norm b)2)"
  unfolding norm_prod_def by simp

  fix r :: real and x y :: "'a × 'b"
  show "norm x = 0 ⟷ x = 0"
    unfolding norm_prod_def
    by (simp add: prod_eq_iff)
  show "norm (x + y) ≤ norm x + norm y"
    unfolding norm_prod_def
    apply (rule order_trans [OF _ real_sqrt_sum_squares_triangle_ineq])
    apply (simp add: add_mono power_mono norm_triangle_ineq)
  show "norm (scaleR r x) = ¦r¦ * norm x"
    unfolding norm_prod_def
    apply (simp add: power_mult_distrib)
    apply (simp add: distrib_left [symmetric])
    apply (simp add: real_sqrt_mult_distrib)
  show "sgn x = scaleR (inverse (norm x)) x"
    by (rule sgn_prod_def)
  show "dist x y = norm (x - y)"
    unfolding dist_prod_def norm_prod_def
    by (simp add: dist_norm)


declare [[code abort: "norm::('a::real_normed_vector*'b::real_normed_vector) ⇒ real"]]

instance prod :: (banach, banach) banach ..

subsubsection ‹Pair operations are linear›

lemma bounded_linear_fst: "bounded_linear fst"
  using fst_add fst_scaleR
  by (rule bounded_linear_intro [where K=1], simp add: norm_prod_def)

lemma bounded_linear_snd: "bounded_linear snd"
  using snd_add snd_scaleR
  by (rule bounded_linear_intro [where K=1], simp add: norm_prod_def)

lemmas bounded_linear_fst_comp = bounded_linear_fst[THEN bounded_linear_compose]

lemmas bounded_linear_snd_comp = bounded_linear_snd[THEN bounded_linear_compose]

lemma bounded_linear_Pair:
  assumes f: "bounded_linear f"
  assumes g: "bounded_linear g"
  shows "bounded_linear (λx. (f x, g x))"
  interpret f: bounded_linear f by fact
  interpret g: bounded_linear g by fact
  fix x y and r :: real
  show "(f (x + y), g (x + y)) = (f x, g x) + (f y, g y)"
    by (simp add: f.add g.add)
  show "(f (r *R x), g (r *R x)) = r *R (f x, g x)"
    by (simp add: f.scaleR g.scaleR)
  obtain Kf where "0 < Kf" and norm_f: "⋀x. norm (f x) ≤ norm x * Kf"
    using f.pos_bounded by fast
  obtain Kg where "0 < Kg" and norm_g: "⋀x. norm (g x) ≤ norm x * Kg"
    using g.pos_bounded by fast
  have "∀x. norm (f x, g x) ≤ norm x * (Kf + Kg)"
    apply (rule allI)
    apply (simp add: norm_Pair)
    apply (rule order_trans [OF sqrt_add_le_add_sqrt], simp, simp)
    apply (simp add: distrib_left)
    apply (rule add_mono [OF norm_f norm_g])
  then show "∃K. ∀x. norm (f x, g x) ≤ norm x * K" ..

subsubsection ‹Frechet derivatives involving pairs›

lemma has_derivative_Pair [derivative_intros]:
  assumes f: "(f has_derivative f') (at x within s)" and g: "(g has_derivative g') (at x within s)"
  shows "((λx. (f x, g x)) has_derivative (λh. (f' h, g' h))) (at x within s)"
proof (rule has_derivativeI_sandwich[of 1])
  show "bounded_linear (λh. (f' h, g' h))"
    using f g by (intro bounded_linear_Pair has_derivative_bounded_linear)
  let ?Rf = "λy. f y - f x - f' (y - x)"
  let ?Rg = "λy. g y - g x - g' (y - x)"
  let ?R = "λy. ((f y, g y) - (f x, g x) - (f' (y - x), g' (y - x)))"

  show "((λy. norm (?Rf y) / norm (y - x) + norm (?Rg y) / norm (y - x)) ⤏ 0) (at x within s)"
    using f g by (intro tendsto_add_zero) (auto simp: has_derivative_iff_norm)

  fix y :: 'a assume "y ≠ x"
  show "norm (?R y) / norm (y - x) ≤ norm (?Rf y) / norm (y - x) + norm (?Rg y) / norm (y - x)"
    unfolding add_divide_distrib [symmetric]
    by (simp add: norm_Pair divide_right_mono order_trans [OF sqrt_add_le_add_sqrt])
qed simp

lemmas has_derivative_fst [derivative_intros] = bounded_linear.has_derivative [OF bounded_linear_fst]
lemmas has_derivative_snd [derivative_intros] = bounded_linear.has_derivative [OF bounded_linear_snd]

lemma has_derivative_split [derivative_intros]:
  "((λp. f (fst p) (snd p)) has_derivative f') F ⟹ ((λ(a, b). f a b) has_derivative f') F"
  unfolding split_beta' .

subsection ‹Product is an inner product space›

instantiation prod :: (real_inner, real_inner) real_inner

definition inner_prod_def:
  "inner x y = inner (fst x) (fst y) + inner (snd x) (snd y)"

lemma inner_Pair [simp]: "inner (a, b) (c, d) = inner a c + inner b d"
  unfolding inner_prod_def by simp

  fix r :: real
  fix x y z :: "'a::real_inner × 'b::real_inner"
  show "inner x y = inner y x"
    unfolding inner_prod_def
    by (simp add: inner_commute)
  show "inner (x + y) z = inner x z + inner y z"
    unfolding inner_prod_def
    by (simp add: inner_add_left)
  show "inner (scaleR r x) y = r * inner x y"
    unfolding inner_prod_def
    by (simp add: distrib_left)
  show "0 ≤ inner x x"
    unfolding inner_prod_def
    by (intro add_nonneg_nonneg inner_ge_zero)
  show "inner x x = 0 ⟷ x = 0"
    unfolding inner_prod_def prod_eq_iff
    by (simp add: add_nonneg_eq_0_iff)
  show "norm x = sqrt (inner x x)"
    unfolding norm_prod_def inner_prod_def
    by (simp add: power2_norm_eq_inner)


lemma inner_Pair_0: "inner x (0, b) = inner (snd x) b" "inner x (a, 0) = inner (fst x) a"
    by (cases x, simp)+

  fixes x :: "'a::real_normed_vector"
  shows norm_Pair1 [simp]: "norm (0,x) = norm x"
    and norm_Pair2 [simp]: "norm (x,0) = norm x"
by (auto simp: norm_Pair)

lemma norm_commute: "norm (x,y) = norm (y,x)"
  by (simp add: norm_Pair)

lemma norm_fst_le: "norm x ≤ norm (x,y)"
  by (metis dist_fst_le fst_conv fst_zero norm_conv_dist)

lemma norm_snd_le: "norm y ≤ norm (x,y)"
  by (metis dist_snd_le snd_conv snd_zero norm_conv_dist)