(*
File: Data_Structures/Selection.thy
Author: Manuel Eberl, TU München
*)
section \<open>The Median-of-Medians Selection Algorithm\<close>
theory Selection
imports Complex_Main Sorting Time_Funs
begin
text \<open>
Note that there is significant overlap between this theory (which is intended mostly for the
Functional Data Structures book) and the Median-of-Medians AFP entry.
\<close>
subsection \<open>Auxiliary material\<close>
lemma replicate_numeral: "replicate (numeral n) x = x # replicate (pred_numeral n) x"
by (simp add: numeral_eq_Suc)
lemma isort_correct: "isort xs = sort xs"
using sorted_isort mset_isort by (metis properties_for_sort)
lemma sum_list_replicate [simp]: "sum_list (replicate n x) = n * x"
by (induction n) auto
lemma mset_concat: "mset (concat xss) = sum_list (map mset xss)"
by (induction xss) simp_all
lemma set_mset_sum_list [simp]: "set_mset (sum_list xs) = (\<Union>x\<in>set xs. set_mset x)"
by (induction xs) auto
lemma filter_mset_image_mset:
"filter_mset P (image_mset f A) = image_mset f (filter_mset (\<lambda>x. P (f x)) A)"
by (induction A) auto
lemma filter_mset_sum_list: "filter_mset P (sum_list xs) = sum_list (map (filter_mset P) xs)"
by (induction xs) simp_all
lemma sum_mset_mset_mono:
assumes "(\<And>x. x \<in># A \<Longrightarrow> f x \<subseteq># g x)"
shows "(\<Sum>x\<in>#A. f x) \<subseteq># (\<Sum>x\<in>#A. g x)"
using assms by (induction A) (auto intro!: subset_mset.add_mono)
lemma mset_filter_mono:
assumes "A \<subseteq># B" "\<And>x. x \<in># A \<Longrightarrow> P x \<Longrightarrow> Q x"
shows "filter_mset P A \<subseteq># filter_mset Q B"
by (rule mset_subset_eqI) (insert assms, auto simp: mset_subset_eq_count count_eq_zero_iff)
lemma size_mset_sum_mset_distrib: "size (sum_mset A :: 'a multiset) = sum_mset (image_mset size A)"
by (induction A) auto
lemma sum_mset_mono:
assumes "\<And>x. x \<in># A \<Longrightarrow> f x \<le> (g x :: 'a :: {ordered_ab_semigroup_add,comm_monoid_add})"
shows "(\<Sum>x\<in>#A. f x) \<le> (\<Sum>x\<in>#A. g x)"
using assms by (induction A) (auto intro!: add_mono)
lemma filter_mset_is_empty_iff: "filter_mset P A = {#} \<longleftrightarrow> (\<forall>x. x \<in># A \<longrightarrow> \<not>P x)"
by (auto simp: multiset_eq_iff count_eq_zero_iff)
lemma sort_eq_Nil_iff [simp]: "sort xs = [] \<longleftrightarrow> xs = []"
by (metis set_empty set_sort)
lemma sort_mset_cong: "mset xs = mset ys \<Longrightarrow> sort xs = sort ys"
by (metis sorted_list_of_multiset_mset)
lemma Min_set_sorted: "sorted xs \<Longrightarrow> xs \<noteq> [] \<Longrightarrow> Min (set xs) = hd xs"
by (cases xs; force intro: Min_insert2)
lemma hd_sort:
fixes xs :: "'a :: linorder list"
shows "xs \<noteq> [] \<Longrightarrow> hd (sort xs) = Min (set xs)"
by (subst Min_set_sorted [symmetric]) auto
lemma length_filter_conv_size_filter_mset: "length (filter P xs) = size (filter_mset P (mset xs))"
by (induction xs) auto
lemma sorted_filter_less_subset_take:
assumes "sorted xs" and "i < length xs"
shows "{#x \<in># mset xs. x < xs ! i#} \<subseteq># mset (take i xs)"
using assms
proof (induction xs arbitrary: i rule: list.induct)
case (Cons x xs i)
show ?case
proof (cases i)
case 0
thus ?thesis using Cons.prems by (auto simp: filter_mset_is_empty_iff)
next
case (Suc i')
have "{#y \<in># mset (x # xs). y < (x # xs) ! i#} \<subseteq># add_mset x {#y \<in># mset xs. y < xs ! i'#}"
using Suc Cons.prems by (auto)
also have "\<dots> \<subseteq># add_mset x (mset (take i' xs))"
unfolding mset_subset_eq_add_mset_cancel using Cons.prems Suc
by (intro Cons.IH) (auto)
also have "\<dots> = mset (take i (x # xs))" by (simp add: Suc)
finally show ?thesis .
qed
qed auto
lemma sorted_filter_greater_subset_drop:
assumes "sorted xs" and "i < length xs"
shows "{#x \<in># mset xs. x > xs ! i#} \<subseteq># mset (drop (Suc i) xs)"
using assms
proof (induction xs arbitrary: i rule: list.induct)
case (Cons x xs i)
show ?case
proof (cases i)
case 0
thus ?thesis by (auto simp: sorted_append filter_mset_is_empty_iff)
next
case (Suc i')
have "{#y \<in># mset (x # xs). y > (x # xs) ! i#} \<subseteq># {#y \<in># mset xs. y > xs ! i'#}"
using Suc Cons.prems by (auto simp: set_conv_nth)
also have "\<dots> \<subseteq># mset (drop (Suc i') xs)"
using Cons.prems Suc by (intro Cons.IH) (auto)
also have "\<dots> = mset (drop (Suc i) (x # xs))" by (simp add: Suc)
finally show ?thesis .
qed
qed auto
subsection \<open>Chopping a list into equally-sized bits\<close>
fun chop :: "nat \<Rightarrow> 'a list \<Rightarrow> 'a list list" where
"chop 0 _ = []"
| "chop _ [] = []"
| "chop n xs = take n xs # chop n (drop n xs)"
lemmas [simp del] = chop.simps
text \<open>
This is an alternative induction rule for \<^const>\<open>chop\<close>, which is often nicer to use.
\<close>
lemma chop_induct' [case_names trivial reduce]:
assumes "\<And>n xs. n = 0 \<or> xs = [] \<Longrightarrow> P n xs"
assumes "\<And>n xs. n > 0 \<Longrightarrow> xs \<noteq> [] \<Longrightarrow> P n (drop n xs) \<Longrightarrow> P n xs"
shows "P n xs"
using assms
proof induction_schema
show "wf (measure (length \<circ> snd))"
by auto
qed (blast | simp)+
lemma chop_eq_Nil_iff [simp]: "chop n xs = [] \<longleftrightarrow> n = 0 \<or> xs = []"
by (induction n xs rule: chop.induct; subst chop.simps) auto
lemma chop_0 [simp]: "chop 0 xs = []"
by (simp add: chop.simps)
lemma chop_Nil [simp]: "chop n [] = []"
by (cases n) (auto simp: chop.simps)
lemma chop_reduce: "n > 0 \<Longrightarrow> xs \<noteq> [] \<Longrightarrow> chop n xs = take n xs # chop n (drop n xs)"
by (cases n; cases xs) (auto simp: chop.simps)
lemma concat_chop [simp]: "n > 0 \<Longrightarrow> concat (chop n xs) = xs"
by (induction n xs rule: chop.induct; subst chop.simps) auto
lemma chop_elem_not_Nil [dest]: "ys \<in> set (chop n xs) \<Longrightarrow> ys \<noteq> []"
by (induction n xs rule: chop.induct; subst (asm) chop.simps)
(auto simp: eq_commute[of "[]"] split: if_splits)
lemma length_chop_part_le: "ys \<in> set (chop n xs) \<Longrightarrow> length ys \<le> n"
by (induction n xs rule: chop.induct; subst (asm) chop.simps) (auto split: if_splits)
lemma length_chop:
assumes "n > 0"
shows "length (chop n xs) = nat \<lceil>length xs / n\<rceil>"
proof -
from \<open>n > 0\<close> have "real n * length (chop n xs) \<ge> length xs"
by (induction n xs rule: chop.induct; subst chop.simps) (auto simp: field_simps)
moreover from \<open>n > 0\<close> have "real n * length (chop n xs) < length xs + n"
by (induction n xs rule: chop.induct; subst chop.simps)
(auto simp: field_simps split: nat_diff_split_asm)+
ultimately have "length (chop n xs) \<ge> length xs / n" and "length (chop n xs) < length xs / n + 1"
using assms by (auto simp: field_simps)
thus ?thesis by linarith
qed
lemma sum_msets_chop: "n > 0 \<Longrightarrow> (\<Sum>ys\<leftarrow>chop n xs. mset ys) = mset xs"
by (subst mset_concat [symmetric]) simp_all
lemma UN_sets_chop: "n > 0 \<Longrightarrow> (\<Union>ys\<in>set (chop n xs). set ys) = set xs"
by (simp only: set_concat [symmetric] concat_chop)
lemma chop_append: "d dvd length xs \<Longrightarrow> chop d (xs @ ys) = chop d xs @ chop d ys"
by (induction d xs rule: chop_induct') (auto simp: chop_reduce dvd_imp_le)
lemma chop_replicate [simp]: "d > 0 \<Longrightarrow> chop d (replicate d xs) = [replicate d xs]"
by (subst chop_reduce) auto
lemma chop_replicate_dvd [simp]:
assumes "d dvd n"
shows "chop d (replicate n x) = replicate (n div d) (replicate d x)"
proof (cases "d = 0")
case False
from assms obtain k where k: "n = d * k"
by blast
have "chop d (replicate (d * k) x) = replicate k (replicate d x)"
using False by (induction k) (auto simp: replicate_add chop_append)
thus ?thesis using False by (simp add: k)
qed auto
lemma chop_concat:
assumes "\<forall>xs\<in>set xss. length xs = d" and "d > 0"
shows "chop d (concat xss) = xss"
using assms
proof (induction xss)
case (Cons xs xss)
have "chop d (concat (xs # xss)) = chop d (xs @ concat xss)"
by simp
also have "\<dots> = chop d xs @ chop d (concat xss)"
using Cons.prems by (intro chop_append) auto
also have "chop d xs = [xs]"
using Cons.prems by (subst chop_reduce) auto
also have "chop d (concat xss) = xss"
using Cons.prems by (intro Cons.IH) auto
finally show ?case by simp
qed auto
subsection \<open>Selection\<close>
definition select :: "nat \<Rightarrow> ('a :: linorder) list \<Rightarrow> 'a" where
"select k xs = sort xs ! k"
lemma select_0: "xs \<noteq> [] \<Longrightarrow> select 0 xs = Min (set xs)"
by (simp add: hd_sort select_def flip: hd_conv_nth)
lemma select_mset_cong: "mset xs = mset ys \<Longrightarrow> select k xs = select k ys"
using sort_mset_cong[of xs ys] unfolding select_def by auto
lemma select_in_set [intro,simp]:
assumes "k < length xs"
shows "select k xs \<in> set xs"
proof -
from assms have "sort xs ! k \<in> set (sort xs)" by (intro nth_mem) auto
also have "set (sort xs) = set xs" by simp
finally show ?thesis by (simp add: select_def)
qed
lemma
assumes "n < length xs"
shows size_less_than_select: "size {#y \<in># mset xs. y < select n xs#} \<le> n"
and size_greater_than_select: "size {#y \<in># mset xs. y > select n xs#} < length xs - n"
proof -
have "size {#y \<in># mset (sort xs). y < select n xs#} \<le> size (mset (take n (sort xs)))"
unfolding select_def using assms
by (intro size_mset_mono sorted_filter_less_subset_take) auto
thus "size {#y \<in># mset xs. y < select n xs#} \<le> n"
by simp
have "size {#y \<in># mset (sort xs). y > select n xs#} \<le> size (mset (drop (Suc n) (sort xs)))"
unfolding select_def using assms
by (intro size_mset_mono sorted_filter_greater_subset_drop) auto
thus "size {#y \<in># mset xs. y > select n xs#} < length xs - n"
using assms by simp
qed
subsection \<open>The designated median of a list\<close>
definition median where "median xs = select ((length xs - 1) div 2) xs"
lemma median_in_set [intro, simp]:
assumes "xs \<noteq> []"
shows "median xs \<in> set xs"
proof -
from assms have "length xs > 0" by auto
hence "(length xs - 1) div 2 < length xs" by linarith
thus ?thesis by (simp add: median_def)
qed
lemma size_less_than_median: "size {#y \<in># mset xs. y < median xs#} \<le> (length xs - 1) div 2"
proof (cases "xs = []")
case False
hence "length xs > 0"
by auto
hence less: "(length xs - 1) div 2 < length xs"
by linarith
show "size {#y \<in># mset xs. y < median xs#} \<le> (length xs - 1) div 2"
using size_less_than_select[OF less] by (simp add: median_def)
qed auto
lemma size_greater_than_median: "size {#y \<in># mset xs. y > median xs#} \<le> length xs div 2"
proof (cases "xs = []")
case False
hence "length xs > 0"
by auto
hence less: "(length xs - 1) div 2 < length xs"
by linarith
have "size {#y \<in># mset xs. y > median xs#} < length xs - (length xs - 1) div 2"
using size_greater_than_select[OF less] by (simp add: median_def)
also have "\<dots> = length xs div 2 + 1"
using \<open>length xs > 0\<close> by linarith
finally show "size {#y \<in># mset xs. y > median xs#} \<le> length xs div 2"
by simp
qed auto
lemmas median_props = size_less_than_median size_greater_than_median
subsection \<open>A recurrence for selection\<close>
definition partition3 :: "'a \<Rightarrow> 'a :: linorder list \<Rightarrow> 'a list \<times> 'a list \<times> 'a list" where
"partition3 x xs = (filter (\<lambda>y. y < x) xs, filter (\<lambda>y. y = x) xs, filter (\<lambda>y. y > x) xs)"
lemma partition3_code [code]:
"partition3 x [] = ([], [], [])"
"partition3 x (y # ys) =
(case partition3 x ys of (ls, es, gs) \<Rightarrow>
if y < x then (y # ls, es, gs) else if x = y then (ls, y # es, gs) else (ls, es, y # gs))"
by (auto simp: partition3_def)
lemma sort_append:
assumes "\<forall>x\<in>set xs. \<forall>y\<in>set ys. x \<le> y"
shows "sort (xs @ ys) = sort xs @ sort ys"
using assms by (intro properties_for_sort) (auto simp: sorted_append)
lemma select_append:
assumes "\<forall>y\<in>set ys. \<forall>z\<in>set zs. y \<le> z"
shows "k < length ys \<Longrightarrow> select k (ys @ zs) = select k ys"
and "k \<in> {length ys..<length ys + length zs} \<Longrightarrow>
select k (ys @ zs) = select (k - length ys) zs"
using assms by (simp_all add: select_def sort_append nth_append)
lemma select_append':
assumes "\<forall>y\<in>set ys. \<forall>z\<in>set zs. y \<le> z" and "k < length ys + length zs"
shows "select k (ys @ zs) = (if k < length ys then select k ys else select (k - length ys) zs)"
using assms by (auto intro!: select_append)
theorem select_rec_partition:
assumes "k < length xs"
shows "select k xs = (
let (ls, es, gs) = partition3 x xs
in
if k < length ls then select k ls
else if k < length ls + length es then x
else select (k - length ls - length es) gs
)" (is "_ = ?rhs")
proof -
define ls es gs where "ls = filter (\<lambda>y. y < x) xs" and "es = filter (\<lambda>y. y = x) xs"
and "gs = filter (\<lambda>y. y > x) xs"
define nl ne where [simp]: "nl = length ls" "ne = length es"
have mset_eq: "mset xs = mset ls + mset es + mset gs"
unfolding ls_def es_def gs_def by (induction xs) auto
have length_eq: "length xs = length ls + length es + length gs"
unfolding ls_def es_def gs_def
using [[simp_depth_limit = 1]] by (induction xs) auto
have [simp]: "select i es = x" if "i < length es" for i
proof -
have "select i es \<in> set (sort es)" unfolding select_def
using that by (intro nth_mem) auto
thus ?thesis
by (auto simp: es_def)
qed
have "select k xs = select k (ls @ (es @ gs))"
by (intro select_mset_cong) (simp_all add: mset_eq)
also have "\<dots> = (if k < nl then select k ls else select (k - nl) (es @ gs))"
unfolding nl_ne_def using assms
by (intro select_append') (auto simp: ls_def es_def gs_def length_eq)
also have "\<dots> = (if k < nl then select k ls else if k < nl + ne then x
else select (k - nl - ne) gs)"
proof (rule if_cong)
assume "\<not>k < nl"
have "select (k - nl) (es @ gs) =
(if k - nl < ne then select (k - nl) es else select (k - nl - ne) gs)"
unfolding nl_ne_def using assms \<open>\<not>k < nl\<close>
by (intro select_append') (auto simp: ls_def es_def gs_def length_eq)
also have "\<dots> = (if k < nl + ne then x else select (k - nl - ne) gs)"
using \<open>\<not>k < nl\<close> by auto
finally show "select (k - nl) (es @ gs) = \<dots>" .
qed simp_all
also have "\<dots> = ?rhs"
by (simp add: partition3_def ls_def es_def gs_def)
finally show ?thesis .
qed
subsection \<open>The size of the lists in the recursive calls\<close>
text \<open>
We now derive an upper bound for the number of elements of a list that are smaller
(resp. bigger) than the median of medians with chopping size 5. To avoid having to do the
same proof twice, we do it generically for an operation \<open>\<prec>\<close> that we will later instantiate
with either \<open><\<close> or \<open>>\<close>.
\<close>
context
fixes xs :: "'a :: linorder list"
fixes M defines "M \<equiv> median (map median (chop 5 xs))"
begin
lemma size_median_of_medians_aux:
fixes R :: "'a :: linorder \<Rightarrow> 'a \<Rightarrow> bool" (infix "\<prec>" 50)
assumes R: "R \<in> {(<), (>)}"
shows "size {#y \<in># mset xs. y \<prec> M#} \<le> nat \<lceil>0.7 * length xs + 3\<rceil>"
proof -
define n and m where [simp]: "n = length xs" and "m = length (chop 5 xs)"
text \<open>We define an abbreviation for the multiset of all the chopped-up groups:\<close>
text \<open>We then split that multiset into those groups whose medians is less than @{term M}
and the rest.\<close>
define Y_small ("Y\<^sub>\<prec>") where "Y\<^sub>\<prec> = filter_mset (\<lambda>ys. median ys \<prec> M) (mset (chop 5 xs))"
define Y_big ("Y\<^sub>\<succeq>") where "Y\<^sub>\<succeq> = filter_mset (\<lambda>ys. \<not>(median ys \<prec> M)) (mset (chop 5 xs))"
have "m = size (mset (chop 5 xs))" by (simp add: m_def)
also have "mset (chop 5 xs) = Y\<^sub>\<prec> + Y\<^sub>\<succeq>" unfolding Y_small_def Y_big_def
by (rule multiset_partition)
finally have m_eq: "m = size Y\<^sub>\<prec> + size Y\<^sub>\<succeq>" by simp
text \<open>At most half of the lists have a median that is smaller than the median of medians:\<close>
have "size Y\<^sub>\<prec> = size (image_mset median Y\<^sub>\<prec>)" by simp
also have "image_mset median Y\<^sub>\<prec> = {#y \<in># mset (map median (chop 5 xs)). y \<prec> M#}"
unfolding Y_small_def by (subst filter_mset_image_mset [symmetric]) simp_all
also have "size \<dots> \<le> (length (map median (chop 5 xs))) div 2"
unfolding M_def using median_props[of "map median (chop 5 xs)"] R by auto
also have "\<dots> = m div 2" by (simp add: m_def)
finally have size_Y\<^sub>_small: "size Y\<^sub>\<prec> \<le> m div 2" .
text \<open>We estimate the number of elements less than @{term M} by grouping them into elements
coming from @{term "Y\<^sub>\<prec>"} and elements coming from @{term "Y\<^sub>\<succeq>"}:\<close>
have "{#y \<in># mset xs. y \<prec> M#} = {#y \<in># (\<Sum>ys\<leftarrow>chop 5 xs. mset ys). y \<prec> M#}"
by (subst sum_msets_chop) simp_all
also have "\<dots> = (\<Sum>ys\<leftarrow>chop 5 xs. {#y \<in># mset ys. y \<prec> M#})"
by (subst filter_mset_sum_list) (simp add: o_def)
also have "\<dots> = (\<Sum>ys\<in>#mset (chop 5 xs). {#y \<in># mset ys. y \<prec> M#})"
by (subst sum_mset_sum_list [symmetric]) simp_all
also have "mset (chop 5 xs) = Y\<^sub>\<prec> + Y\<^sub>\<succeq>"
by (simp add: Y_small_def Y_big_def not_le)
also have "(\<Sum>ys\<in>#\<dots>. {#y \<in># mset ys. y \<prec> M#}) =
(\<Sum>ys\<in>#Y\<^sub>\<prec>. {#y \<in># mset ys. y \<prec> M#}) + (\<Sum>ys\<in>#Y\<^sub>\<succeq>. {#y \<in># mset ys. y \<prec> M#})"
by simp
text \<open>Next, we overapproximate the elements contributed by @{term "Y\<^sub>\<prec>"}: instead of those elements
that are smaller than the median, we take \<^emph>\<open>all\<close> the elements of each group.
For the elements contributed by @{term "Y\<^sub>\<succeq>"}, we overapproximate by taking all those that
are less than their median instead of only those that are less than @{term M}.\<close>
also have "\<dots> \<subseteq># (\<Sum>ys\<in>#Y\<^sub>\<prec>. mset ys) + (\<Sum>ys\<in>#Y\<^sub>\<succeq>. {#y \<in># mset ys. y \<prec> median ys#})"
using R
by (intro subset_mset.add_mono sum_mset_mset_mono mset_filter_mono) (auto simp: Y_big_def)
finally have "size {# y \<in># mset xs. y \<prec> M#} \<le> size \<dots>"
by (rule size_mset_mono)
hence "size {# y \<in># mset xs. y \<prec> M#} \<le>
(\<Sum>ys\<in>#Y\<^sub>\<prec>. length ys) + (\<Sum>ys\<in>#Y\<^sub>\<succeq>. size {#y \<in># mset ys. y \<prec> median ys#})"
by (simp add: size_mset_sum_mset_distrib multiset.map_comp o_def)
text \<open>Next, we further overapproximate the first sum by noting that each group has
at most size 5.\<close>
also have "(\<Sum>ys\<in>#Y\<^sub>\<prec>. length ys) \<le> (\<Sum>ys\<in>#Y\<^sub>\<prec>. 5)"
by (intro sum_mset_mono) (auto simp: Y_small_def length_chop_part_le)
also have "\<dots> = 5 * size Y\<^sub>\<prec>" by simp
text \<open>Next, we note that each group in @{term "Y\<^sub>\<succeq>"} can have at most 2 elements that are
smaller than its median.\<close>
also have "(\<Sum>ys\<in>#Y\<^sub>\<succeq>. size {#y \<in># mset ys. y \<prec> median ys#}) \<le>
(\<Sum>ys\<in>#Y\<^sub>\<succeq>. length ys div 2)"
proof (intro sum_mset_mono, goal_cases)
fix ys assume "ys \<in># Y\<^sub>\<succeq>"
hence "ys \<noteq> []"
by (auto simp: Y_big_def)
thus "size {#y \<in># mset ys. y \<prec> median ys#} \<le> length ys div 2"
using R median_props[of ys] by auto
qed
also have "\<dots> \<le> (\<Sum>ys\<in>#Y\<^sub>\<succeq>. 2)"
by (intro sum_mset_mono div_le_mono diff_le_mono)
(auto simp: Y_big_def dest: length_chop_part_le)
also have "\<dots> = 2 * size Y\<^sub>\<succeq>" by simp
text \<open>Simplifying gives us the main result.\<close>
also have "5 * size Y\<^sub>\<prec> + 2 * size Y\<^sub>\<succeq> = 2 * m + 3 * size Y\<^sub>\<prec>"
by (simp add: m_eq)
also have "\<dots> \<le> 3.5 * m"
using \<open>size Y\<^sub>\<prec> \<le> m div 2\<close> by linarith
also have "\<dots> = 3.5 * \<lceil>n / 5\<rceil>"
by (simp add: m_def length_chop)
also have "\<dots> \<le> 0.7 * n + 3.5"
by linarith
finally have "size {#y \<in># mset xs. y \<prec> M#} \<le> 0.7 * n + 3.5"
by simp
thus "size {#y \<in># mset xs. y \<prec> M#} \<le> nat \<lceil>0.7 * n + 3\<rceil>"
by linarith
qed
lemma size_less_than_median_of_medians:
"size {#y \<in># mset xs. y < M#} \<le> nat \<lceil>0.7 * length xs + 3\<rceil>"
using size_median_of_medians_aux[of "(<)"] by simp
lemma size_greater_than_median_of_medians:
"size {#y \<in># mset xs. y > M#} \<le> nat \<lceil>0.7 * length xs + 3\<rceil>"
using size_median_of_medians_aux[of "(>)"] by simp
end
subsection \<open>Efficient algorithm\<close>
text \<open>
We handle the base cases and computing the median for the chopped-up sublists of size 5
using the naive selection algorithm where we sort the list using insertion sort.
\<close>
definition slow_select where
"slow_select k xs = isort xs ! k"
definition slow_median where
"slow_median xs = slow_select ((length xs - 1) div 2) xs"
lemma slow_select_correct: "slow_select k xs = select k xs"
by (simp add: slow_select_def select_def isort_correct)
lemma slow_median_correct: "slow_median xs = median xs"
by (simp add: median_def slow_median_def slow_select_correct)
text \<open>
The definition of the selection algorithm is complicated somewhat by the fact that its
termination is contingent on its correctness: if the first recursive call were to return an
element for \<open>x\<close> that is e.g. smaller than all list elements, the algorithm would not terminate.
Therefore, we first prove partial correctness, then termination, and then combine the two
to obtain total correctness.
\<close>
function mom_select where
"mom_select k xs = (
if length xs \<le> 20 then
slow_select k xs
else
let M = mom_select (((length xs + 4) div 5 - 1) div 2) (map slow_median (chop 5 xs));
(ls, es, gs) = partition3 M xs
in
if k < length ls then mom_select k ls
else if k < length ls + length es then M
else mom_select (k - length ls - length es) gs
)"
by auto
text \<open>
If @{const "mom_select"} terminates, it agrees with @{const select}:
\<close>
lemma mom_select_correct_aux:
assumes "mom_select_dom (k, xs)" and "k < length xs"
shows "mom_select k xs = select k xs"
using assms
proof (induction rule: mom_select.pinduct)
case (1 k xs)
show "mom_select k xs = select k xs"
proof (cases "length xs \<le> 20")
case True
thus "mom_select k xs = select k xs" using "1.prems" "1.hyps"
by (subst mom_select.psimps) (auto simp: select_def slow_select_correct)
next
case False
define x where
"x = mom_select (((length xs + 4) div 5 - 1) div 2) (map slow_median (chop 5 xs))"
define ls es gs where "ls = filter (\<lambda>y. y < x) xs" and "es = filter (\<lambda>y. y = x) xs"
and "gs = filter (\<lambda>y. y > x) xs"
define nl ne where "nl = length ls" and "ne = length es"
note defs = nl_def ne_def x_def ls_def es_def gs_def
have tw: "(ls, es, gs) = partition3 x xs"
unfolding partition3_def defs One_nat_def ..
have length_eq: "length xs = nl + ne + length gs"
unfolding nl_def ne_def ls_def es_def gs_def
using [[simp_depth_limit = 1]] by (induction xs) auto
note IH = "1.IH"(2,3)[OF False x_def tw refl refl]
have "mom_select k xs = (if k < nl then mom_select k ls else if k < nl + ne then x
else mom_select (k - nl - ne) gs)" using "1.hyps" False
by (subst mom_select.psimps) (simp_all add: partition3_def flip: defs One_nat_def)
also have "\<dots> = (if k < nl then select k ls else if k < nl + ne then x
else select (k - nl - ne) gs)"
using IH length_eq "1.prems" by (simp add: ls_def es_def gs_def nl_def ne_def)
also have "\<dots> = select k xs" using \<open>k < length xs\<close>
by (subst (3) select_rec_partition[of _ _ x]) (simp_all add: nl_def ne_def flip: tw)
finally show "mom_select k xs = select k xs" .
qed
qed
text \<open>
@{const mom_select} indeed terminates for all inputs:
\<close>
lemma mom_select_termination: "All mom_select_dom"
proof (relation "measure (length \<circ> snd)"; (safe)?)
fix k :: nat and xs :: "'a list"
assume "\<not> length xs \<le> 20"
thus "((((length xs + 4) div 5 - 1) div 2, map slow_median (chop 5 xs)), k, xs)
\<in> measure (length \<circ> snd)"
by (auto simp: length_chop nat_less_iff ceiling_less_iff)
next
fix k :: nat and xs ls es gs :: "'a list"
define x where "x = mom_select (((length xs + 4) div 5 - 1) div 2) (map slow_median (chop 5 xs))"
assume A: "\<not> length xs \<le> 20"
"(ls, es, gs) = partition3 x xs"
"mom_select_dom (((length xs + 4) div 5 - 1) div 2,
map slow_median (chop 5 xs))"
have less: "((length xs + 4) div 5 - 1) div 2 < nat \<lceil>length xs / 5\<rceil>"
using A(1) by linarith
text \<open>For termination, it suffices to prove that @{term x} is in the list.\<close>
have "x = select (((length xs + 4) div 5 - 1) div 2) (map slow_median (chop 5 xs))"
using less unfolding x_def by (intro mom_select_correct_aux A) (auto simp: length_chop)
also have "\<dots> \<in> set (map slow_median (chop 5 xs))"
using less by (intro select_in_set) (simp_all add: length_chop)
also have "\<dots> \<subseteq> set xs"
unfolding set_map
proof safe
fix ys assume ys: "ys \<in> set (chop 5 xs)"
hence "median ys \<in> set ys"
by auto
also have "set ys \<subseteq> \<Union>(set ` set (chop 5 xs))"
using ys by blast
also have "\<dots> = set xs"
by (rule UN_sets_chop) simp_all
finally show "slow_median ys \<in> set xs"
by (simp add: slow_median_correct)
qed
finally have "x \<in> set xs" .
thus "((k, ls), k, xs) \<in> measure (length \<circ> snd)"
and "((k - length ls - length es, gs), k, xs) \<in> measure (length \<circ> snd)"
using A(1,2) by (auto simp: partition3_def intro!: length_filter_less[of x])
qed
termination mom_select by (rule mom_select_termination)
lemmas [simp del] = mom_select.simps
lemma mom_select_correct: "k < length xs \<Longrightarrow> mom_select k xs = select k xs"
using mom_select_correct_aux and mom_select_termination by blast
subsection \<open>Running time analysis\<close>
fun T_partition3 :: "'a \<Rightarrow> 'a list \<Rightarrow> nat" where
"T_partition3 x [] = 1"
| "T_partition3 x (y # ys) = T_partition3 x ys + 1"
lemma T_partition3_eq: "T_partition3 x xs = length xs + 1"
by (induction x xs rule: T_partition3.induct) auto
definition T_slow_select :: "nat \<Rightarrow> 'a :: linorder list \<Rightarrow> nat" where
"T_slow_select k xs = T_isort xs + T_nth (isort xs) k + 1"
definition T_slow_median :: "'a :: linorder list \<Rightarrow> nat" where
"T_slow_median xs = T_slow_select ((length xs - 1) div 2) xs + 1"
lemma T_slow_select_le: "T_slow_select k xs \<le> length xs ^ 2 + 3 * length xs + 3"
proof -
have "T_slow_select k xs \<le> (length xs + 1)\<^sup>2 + (length (isort xs) + 1) + 1"
unfolding T_slow_select_def
by (intro add_mono T_isort_length) (auto simp: T_nth_eq)
also have "\<dots> = length xs ^ 2 + 3 * length xs + 3"
by (simp add: isort_correct algebra_simps power2_eq_square)
finally show ?thesis .
qed
lemma T_slow_median_le: "T_slow_median xs \<le> length xs ^ 2 + 3 * length xs + 4"
unfolding T_slow_median_def using T_slow_select_le[of "(length xs - 1) div 2" xs] by simp
fun T_chop :: "nat \<Rightarrow> 'a list \<Rightarrow> nat" where
"T_chop 0 _ = 1"
| "T_chop _ [] = 1"
| "T_chop n xs = T_take n xs + T_drop n xs + T_chop n (drop n xs)"
lemmas [simp del] = T_chop.simps
lemma T_chop_Nil [simp]: "T_chop d [] = 1"
by (cases d) (auto simp: T_chop.simps)
lemma T_chop_0 [simp]: "T_chop 0 xs = 1"
by (auto simp: T_chop.simps)
lemma T_chop_reduce:
"n > 0 \<Longrightarrow> xs \<noteq> [] \<Longrightarrow> T_chop n xs = T_take n xs + T_drop n xs + T_chop n (drop n xs)"
by (cases n; cases xs) (auto simp: T_chop.simps)
lemma T_chop_le: "T_chop d xs \<le> 5 * length xs + 1"
by (induction d xs rule: T_chop.induct) (auto simp: T_chop_reduce T_take_eq T_drop_eq)
text \<open>
The option \<open>domintros\<close> here allows us to explicitly reason about where the function does and
does not terminate. With this, we can skip the termination proof this time because we can
reuse the one for \<^const>\<open>mom_select\<close>.
\<close>
function (domintros) T_mom_select :: "nat \<Rightarrow> 'a :: linorder list \<Rightarrow> nat" where
"T_mom_select k xs = (
if length xs \<le> 20 then
T_slow_select k xs
else
let xss = chop 5 xs;
ms = map slow_median xss;
idx = (((length xs + 4) div 5 - 1) div 2);
x = mom_select idx ms;
(ls, es, gs) = partition3 x xs;
nl = length ls;
ne = length es
in
(if k < nl then T_mom_select k ls
else if k < nl + ne then 0
else T_mom_select (k - nl - ne) gs) +
T_mom_select idx ms + T_chop 5 xs + T_map T_slow_median xss +
T_partition3 x xs + T_length ls + T_length es + 1
)"
by auto
termination T_mom_select
proof (rule allI, safe)
fix k :: nat and xs :: "'a :: linorder list"
have "mom_select_dom (k, xs)"
using mom_select_termination by blast
thus "T_mom_select_dom (k, xs)"
by (induction k xs rule: mom_select.pinduct)
(rule T_mom_select.domintros, simp_all)
qed
lemmas [simp del] = T_mom_select.simps
function T'_mom_select :: "nat \<Rightarrow> nat" where
"T'_mom_select n =
(if n \<le> 20 then
463
else
T'_mom_select (nat \<lceil>0.2*n\<rceil>) + T'_mom_select (nat \<lceil>0.7*n+3\<rceil>) + 17 * n + 50)"
by force+
termination by (relation "measure id"; simp; linarith)
lemmas [simp del] = T'_mom_select.simps
lemma T'_mom_select_ge: "T'_mom_select n \<ge> 463"
by (induction n rule: T'_mom_select.induct; subst T'_mom_select.simps) auto
lemma T'_mom_select_mono:
"m \<le> n \<Longrightarrow> T'_mom_select m \<le> T'_mom_select n"
proof (induction n arbitrary: m rule: less_induct)
case (less n m)
show ?case
proof (cases "m \<le> 20")
case True
hence "T'_mom_select m = 463"
by (subst T'_mom_select.simps) auto
also have "\<dots> \<le> T'_mom_select n"
by (rule T'_mom_select_ge)
finally show ?thesis .
next
case False
hence "T'_mom_select m =
T'_mom_select (nat \<lceil>0.2*m\<rceil>) + T'_mom_select (nat \<lceil>0.7*m + 3\<rceil>) + 17 * m + 50"
by (subst T'_mom_select.simps) auto
also have "\<dots> \<le> T'_mom_select (nat \<lceil>0.2*n\<rceil>) + T'_mom_select (nat \<lceil>0.7*n + 3\<rceil>) + 17 * n + 50"
using \<open>m \<le> n\<close> and False by (intro add_mono less.IH; linarith)
also have "\<dots> = T'_mom_select n"
using \<open>m \<le> n\<close> and False by (subst T'_mom_select.simps) auto
finally show ?thesis .
qed
qed
lemma T_mom_select_le_aux: "T_mom_select k xs \<le> T'_mom_select (length xs)"
proof (induction k xs rule: T_mom_select.induct)
case (1 k xs)
define n where [simp]: "n = length xs"
define x where
"x = mom_select (((length xs + 4) div 5 - 1) div 2) (map slow_median (chop 5 xs))"
define ls es gs where "ls = filter (\<lambda>y. y < x) xs" and "es = filter (\<lambda>y. y = x) xs"
and "gs = filter (\<lambda>y. y > x) xs"
define nl ne where "nl = length ls" and "ne = length es"
note defs = nl_def ne_def x_def ls_def es_def gs_def
have tw: "(ls, es, gs) = partition3 x xs"
unfolding partition3_def defs One_nat_def ..
note IH = "1.IH"(1,2,3)[OF _ refl refl refl x_def tw refl refl refl refl]
show ?case
proof (cases "length xs \<le> 20")
case True \<comment> \<open>base case\<close>
hence "T_mom_select k xs \<le> (length xs)\<^sup>2 + 3 * length xs + 3"
using T_slow_select_le[of k xs] by (subst T_mom_select.simps) auto
also have "\<dots> \<le> 20\<^sup>2 + 3 * 20 + 3"
using True by (intro add_mono power_mono) auto
also have "\<dots> \<le> 463"
by simp
also have "\<dots> = T'_mom_select (length xs)"
using True by (simp add: T'_mom_select.simps)
finally show ?thesis by simp
next
case False \<comment> \<open>recursive case\<close>
have "((n + 4) div 5 - 1) div 2 < nat \<lceil>n / 5\<rceil>"
using False unfolding n_def by linarith
hence "x = select (((n + 4) div 5 - 1) div 2) (map slow_median (chop 5 xs))"
unfolding x_def n_def by (intro mom_select_correct) (auto simp: length_chop)
also have "((n + 4) div 5 - 1) div 2 = (nat \<lceil>n / 5\<rceil> - 1) div 2"
by linarith
also have "select \<dots> (map slow_median (chop 5 xs)) = median (map slow_median (chop 5 xs))"
by (auto simp: median_def length_chop)
finally have x_eq: "x = median (map slow_median (chop 5 xs))" .
text \<open>The cost of computing the medians of all the subgroups:\<close>
define T_ms where "T_ms = T_map T_slow_median (chop 5 xs)"
have "T_ms \<le> 9 * n + 45"
proof -
have "T_ms = (\<Sum>ys\<leftarrow>chop 5 xs. T_slow_median ys) + length (chop 5 xs) + 1"
by (simp add: T_ms_def T_map_eq)
also have "(\<Sum>ys\<leftarrow>chop 5 xs. T_slow_median ys) \<le> (\<Sum>ys\<leftarrow>chop 5 xs. 44)"
proof (intro sum_list_mono)
fix ys assume "ys \<in> set (chop 5 xs)"
hence "length ys \<le> 5"
using length_chop_part_le by blast
have "T_slow_median ys \<le> (length ys) ^ 2 + 3 * length ys + 4"
by (rule T_slow_median_le)
also have "\<dots> \<le> 5 ^ 2 + 3 * 5 + 4"
using \<open>length ys \<le> 5\<close> by (intro add_mono power_mono) auto
finally show "T_slow_median ys \<le> 44" by simp
qed
also have "(\<Sum>ys\<leftarrow>chop 5 xs. 44) + length (chop 5 xs) + 1 =
45 * nat \<lceil>real n / 5\<rceil> + 1"
by (simp add: map_replicate_const length_chop)
also have "\<dots> \<le> 9 * n + 45"
by linarith
finally show "T_ms \<le> 9 * n + 45" by simp
qed
text \<open>The cost of the first recursive call (to compute the median of medians):\<close>
define T_rec1 where
"T_rec1 = T_mom_select (((length xs + 4) div 5 - 1) div 2) (map slow_median (chop 5 xs))"
have "T_rec1 \<le> T'_mom_select (length (map slow_median (chop 5 xs)))"
using False unfolding T_rec1_def by (intro IH(3)) auto
hence "T_rec1 \<le> T'_mom_select (nat \<lceil>0.2 * n\<rceil>)"
by (simp add: length_chop)
text \<open>The cost of the second recursive call (to compute the final result):\<close>
define T_rec2 where "T_rec2 = (if k < nl then T_mom_select k ls
else if k < nl + ne then 0
else T_mom_select (k - nl - ne) gs)"
consider "k < nl" | "k \<in> {nl..<nl+ne}" | "k \<ge> nl+ne"
by force
hence "T_rec2 \<le> T'_mom_select (nat \<lceil>0.7 * n + 3\<rceil>)"
proof cases
assume "k < nl"
hence "T_rec2 = T_mom_select k ls"
by (simp add: T_rec2_def)
also have "\<dots> \<le> T'_mom_select (length ls)"
by (rule IH(1)) (use \<open>k < nl\<close> False in \<open>auto simp: defs\<close>)
also have "length ls \<le> nat \<lceil>0.7 * n + 3\<rceil>"
unfolding ls_def using size_less_than_median_of_medians[of xs]
by (auto simp: length_filter_conv_size_filter_mset slow_median_correct[abs_def] x_eq)
hence "T'_mom_select (length ls) \<le> T'_mom_select (nat \<lceil>0.7 * n + 3\<rceil>)"
by (rule T'_mom_select_mono)
finally show ?thesis .
next
assume "k \<in> {nl..<nl + ne}"
hence "T_rec2 = 0"
by (simp add: T_rec2_def)
thus ?thesis
using T'_mom_select_ge[of "nat \<lceil>0.7 * n + 3\<rceil>"] by simp
next
assume "k \<ge> nl + ne"
hence "T_rec2 = T_mom_select (k - nl - ne) gs"
by (simp add: T_rec2_def)
also have "\<dots> \<le> T'_mom_select (length gs)"
unfolding nl_def ne_def by (rule IH(2)) (use \<open>k \<ge> nl + ne\<close> False in \<open>auto simp: defs\<close>)
also have "length gs \<le> nat \<lceil>0.7 * n + 3\<rceil>"
unfolding gs_def using size_greater_than_median_of_medians[of xs]
by (auto simp: length_filter_conv_size_filter_mset slow_median_correct[abs_def] x_eq)
hence "T'_mom_select (length gs) \<le> T'_mom_select (nat \<lceil>0.7 * n + 3\<rceil>)"
by (rule T'_mom_select_mono)
finally show ?thesis .
qed
text \<open>Now for the final inequality chain:\<close>
have "T_mom_select k xs = T_rec2 + T_rec1 + T_ms + n + nl + ne + T_chop 5 xs + 4" using False
by (subst T_mom_select.simps, unfold Let_def tw [symmetric] defs [symmetric])
(simp_all add: nl_def ne_def T_rec1_def T_rec2_def T_partition3_eq
T_length_eq T_ms_def)
also have "nl \<le> n" by (simp add: nl_def ls_def)
also have "ne \<le> n" by (simp add: ne_def es_def)
also note \<open>T_ms \<le> 9 * n + 45\<close>
also have "T_chop 5 xs \<le> 5 * n + 1"
using T_chop_le[of 5 xs] by simp
also note \<open>T_rec1 \<le> T'_mom_select (nat \<lceil>0.2*n\<rceil>)\<close>
also note \<open>T_rec2 \<le> T'_mom_select (nat \<lceil>0.7*n + 3\<rceil>)\<close>
finally have "T_mom_select k xs \<le>
T'_mom_select (nat \<lceil>0.7*n + 3\<rceil>) + T'_mom_select (nat \<lceil>0.2*n\<rceil>) + 17 * n + 50"
by simp
also have "\<dots> = T'_mom_select n"
using False by (subst T'_mom_select.simps) auto
finally show ?thesis by simp
qed
qed
subsection \<open>Akra--Bazzi Light\<close>
lemma akra_bazzi_light_aux1:
fixes a b :: real and n n0 :: nat
assumes ab: "a > 0" "a < 1" "n > n0"
assumes "n0 \<ge> (max 0 b + 1) / (1 - a)"
shows "nat \<lceil>a*n+b\<rceil> < n"
proof -
have "a * real n + max 0 b \<ge> 0"
using ab by simp
hence "real (nat \<lceil>a*n+b\<rceil>) \<le> a * n + max 0 b + 1"
by linarith
also {
have "n0 \<ge> (max 0 b + 1) / (1 - a)"
by fact
also have "\<dots> < real n"
using assms by simp
finally have "a * real n + max 0 b + 1 < real n"
using ab by (simp add: field_simps)
}
finally show "nat \<lceil>a*n+b\<rceil> < n"
using \<open>n > n0\<close> by linarith
qed
lemma akra_bazzi_light_aux2:
fixes f :: "nat \<Rightarrow> real"
fixes n\<^sub>0 :: nat and a b c d :: real and C1 C2 C\<^sub>1 C\<^sub>2 :: real
assumes bounds: "a > 0" "c > 0" "a + c < 1" "C\<^sub>1 \<ge> 0"
assumes rec: "\<forall>n>n\<^sub>0. f n = f (nat \<lceil>a*n+b\<rceil>) + f (nat \<lceil>c*n+d\<rceil>) + C\<^sub>1 * n + C\<^sub>2"
assumes ineqs: "n\<^sub>0 > (max 0 b + max 0 d + 2) / (1 - a - c)"
"C\<^sub>3 \<ge> C\<^sub>1 / (1 - a - c)"
"C\<^sub>3 \<ge> (C\<^sub>1 * n\<^sub>0 + C\<^sub>2 + C\<^sub>4) / ((1 - a - c) * n\<^sub>0 - max 0 b - max 0 d - 2)"
"\<forall>n\<le>n\<^sub>0. f n \<le> C\<^sub>4"
shows "f n \<le> C\<^sub>3 * n + C\<^sub>4"
proof (induction n rule: less_induct)
case (less n)
have "0 \<le> C\<^sub>1 / (1 - a - c)"
using bounds by auto
also have "\<dots> \<le> C\<^sub>3"
by fact
finally have "C\<^sub>3 \<ge> 0" .
show ?case
proof (cases "n > n\<^sub>0")
case False
hence "f n \<le> C\<^sub>4"
using ineqs(4) by auto
also have "\<dots> \<le> C\<^sub>3 * real n + C\<^sub>4"
using bounds \<open>C\<^sub>3 \<ge> 0\<close> by auto
finally show ?thesis .
next
case True
have nonneg: "a * n \<ge> 0" "c * n \<ge> 0"
using bounds by simp_all
have "(max 0 b + 1) / (1 - a) \<le> (max 0 b + max 0 d + 2) / (1 - a - c)"
using bounds by (intro frac_le) auto
hence "n\<^sub>0 \<ge> (max 0 b + 1) / (1 - a)"
using ineqs(1) by linarith
hence rec_less1: "nat \<lceil>a*n+b\<rceil> < n"
using bounds \<open>n > n\<^sub>0\<close> by (intro akra_bazzi_light_aux1[of _ n\<^sub>0]) auto
have "(max 0 d + 1) / (1 - c) \<le> (max 0 b + max 0 d + 2) / (1 - a - c)"
using bounds by (intro frac_le) auto
hence "n\<^sub>0 \<ge> (max 0 d + 1) / (1 - c)"
using ineqs(1) by linarith
hence rec_less2: "nat \<lceil>c*n+d\<rceil> < n"
using bounds \<open>n > n\<^sub>0\<close> by (intro akra_bazzi_light_aux1[of _ n\<^sub>0]) auto
have "f n = f (nat \<lceil>a*n+b\<rceil>) + f (nat \<lceil>c*n+d\<rceil>) + C\<^sub>1 * n + C\<^sub>2"
using \<open>n > n\<^sub>0\<close> by (subst rec) auto
also have "\<dots> \<le> (C\<^sub>3 * nat \<lceil>a*n+b\<rceil> + C\<^sub>4) + (C\<^sub>3 * nat \<lceil>c*n+d\<rceil> + C\<^sub>4) + C\<^sub>1 * n + C\<^sub>2"
using rec_less1 rec_less2 by (intro add_mono less.IH) auto
also have "\<dots> \<le> (C\<^sub>3 * (a*n+max 0 b+1) + C\<^sub>4) + (C\<^sub>3 * (c*n+max 0 d+1) + C\<^sub>4) + C\<^sub>1 * n + C\<^sub>2"
using bounds \<open>C\<^sub>3 \<ge> 0\<close> nonneg by (intro add_mono mult_left_mono order.refl; linarith)
also have "\<dots> = C\<^sub>3 * n + ((C\<^sub>3 * (max 0 b + max 0 d + 2) + 2 * C\<^sub>4 + C\<^sub>2) -
(C\<^sub>3 * (1 - a - c) - C\<^sub>1) * n)"
by (simp add: algebra_simps)
also have "\<dots> \<le> C\<^sub>3 * n + ((C\<^sub>3 * (max 0 b + max 0 d + 2) + 2 * C\<^sub>4 + C\<^sub>2) -
(C\<^sub>3 * (1 - a - c) - C\<^sub>1) * n\<^sub>0)"
using \<open>n > n\<^sub>0\<close> ineqs(2) bounds
by (intro add_mono diff_mono order.refl mult_left_mono) (auto simp: field_simps)
also have "(C\<^sub>3 * (max 0 b + max 0 d + 2) + 2 * C\<^sub>4 + C\<^sub>2) - (C\<^sub>3 * (1 - a - c) - C\<^sub>1) * n\<^sub>0 \<le> C\<^sub>4"
using ineqs bounds by (simp add: field_simps)
finally show "f n \<le> C\<^sub>3 * real n + C\<^sub>4"
by (simp add: mult_right_mono)
qed
qed
lemma akra_bazzi_light:
fixes f :: "nat \<Rightarrow> real"
fixes n\<^sub>0 :: nat and a b c d C\<^sub>1 C\<^sub>2 :: real
assumes bounds: "a > 0" "c > 0" "a + c < 1" "C\<^sub>1 \<ge> 0"
assumes rec: "\<forall>n>n\<^sub>0. f n = f (nat \<lceil>a*n+b\<rceil>) + f (nat \<lceil>c*n+d\<rceil>) + C\<^sub>1 * n + C\<^sub>2"
shows "\<exists>C\<^sub>3 C\<^sub>4. \<forall>n. f n \<le> C\<^sub>3 * real n + C\<^sub>4"
proof -
define n\<^sub>0' where "n\<^sub>0' = max n\<^sub>0 (nat \<lceil>(max 0 b + max 0 d + 2) / (1 - a - c) + 1\<rceil>)"
define C\<^sub>4 where "C\<^sub>4 = Max (f ` {..n\<^sub>0'})"
define C\<^sub>3 where "C\<^sub>3 = max (C\<^sub>1 / (1 - a - c))
((C\<^sub>1 * n\<^sub>0' + C\<^sub>2 + C\<^sub>4) / ((1 - a - c) * n\<^sub>0' - max 0 b - max 0 d - 2))"
have "f n \<le> C\<^sub>3 * n + C\<^sub>4" for n
proof (rule akra_bazzi_light_aux2[OF bounds _])
show "\<forall>n>n\<^sub>0'. f n = f (nat \<lceil>a*n+b\<rceil>) + f (nat \<lceil>c*n+d\<rceil>) + C\<^sub>1 * n + C\<^sub>2"
using rec by (auto simp: n\<^sub>0'_def)
next
show "C\<^sub>3 \<ge> C\<^sub>1 / (1 - a - c)"
and "C\<^sub>3 \<ge> (C\<^sub>1 * n\<^sub>0' + C\<^sub>2 + C\<^sub>4) / ((1 - a - c) * n\<^sub>0' - max 0 b - max 0 d - 2)"
by (simp_all add: C\<^sub>3_def)
next
have "(max 0 b + max 0 d + 2) / (1 - a - c) < nat \<lceil>(max 0 b + max 0 d + 2) / (1 - a - c) + 1\<rceil>"
by linarith
also have "\<dots> \<le> n\<^sub>0'"
by (simp add: n\<^sub>0'_def)
finally show "(max 0 b + max 0 d + 2) / (1 - a - c) < real n\<^sub>0'" .
next
show "\<forall>n\<le>n\<^sub>0'. f n \<le> C\<^sub>4"
by (auto simp: C\<^sub>4_def)
qed
thus ?thesis by blast
qed
lemma akra_bazzi_light_nat:
fixes f :: "nat \<Rightarrow> nat"
fixes n\<^sub>0 :: nat and a b c d :: real and C\<^sub>1 C\<^sub>2 :: nat
assumes bounds: "a > 0" "c > 0" "a + c < 1" "C\<^sub>1 \<ge> 0"
assumes rec: "\<forall>n>n\<^sub>0. f n = f (nat \<lceil>a*n+b\<rceil>) + f (nat \<lceil>c*n+d\<rceil>) + C\<^sub>1 * n + C\<^sub>2"
shows "\<exists>C\<^sub>3 C\<^sub>4. \<forall>n. f n \<le> C\<^sub>3 * n + C\<^sub>4"
proof -
have "\<exists>C\<^sub>3 C\<^sub>4. \<forall>n. real (f n) \<le> C\<^sub>3 * real n + C\<^sub>4"
using assms by (intro akra_bazzi_light[of a c C\<^sub>1 n\<^sub>0 f b d C\<^sub>2]) auto
then obtain C\<^sub>3 C\<^sub>4 where le: "\<forall>n. real (f n) \<le> C\<^sub>3 * real n + C\<^sub>4"
by blast
have "f n \<le> nat \<lceil>C\<^sub>3\<rceil> * n + nat \<lceil>C\<^sub>4\<rceil>" for n
proof -
have "real (f n) \<le> C\<^sub>3 * real n + C\<^sub>4"
using le by blast
also have "\<dots> \<le> real (nat \<lceil>C\<^sub>3\<rceil>) * real n + real (nat \<lceil>C\<^sub>4\<rceil>)"
by (intro add_mono mult_right_mono; linarith)
also have "\<dots> = real (nat \<lceil>C\<^sub>3\<rceil> * n + nat \<lceil>C\<^sub>4\<rceil>)"
by simp
finally show ?thesis by linarith
qed
thus ?thesis by blast
qed
lemma T'_mom_select_le': "\<exists>C\<^sub>1 C\<^sub>2. \<forall>n. T'_mom_select n \<le> C\<^sub>1 * n + C\<^sub>2"
proof (rule akra_bazzi_light_nat)
show "\<forall>n>20. T'_mom_select n = T'_mom_select (nat \<lceil>0.2 * n + 0\<rceil>) +
T'_mom_select (nat \<lceil>0.7 * n + 3\<rceil>) + 17 * n + 50"
using T'_mom_select.simps by auto
qed auto
end