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Functional Data Structures with Isabelle/HOL Functional Data Structures with Isabelle/HOL Tobias Nipkow Fakultät für Informatik Technische Universität München 2021-6-14 1 Part II Functional Data Structures 2 Chapter 6 Sorting 3 1 Correctness 2 Insertion Sort 3 Time 4 Merge Sort 4 1 Correctness 2 Insertion Sort 3 Time 4 Merge Sort 5 sorted :: ( 0a::linorder) list ) bool sorted [] = True sorted (x # ys) = ((8 y2set ys: x ≤ y) ^ sorted ys) 6 Correctness of sorting Specification of sort :: ( 0a::linorder) list ) 0a list: sorted (sort xs) Is that it? How about set (sort xs) = set xs Better: every x occurs as often in sort xs as in xs. More succinctly: mset (sort xs) = mset xs where mset :: 0a list ) 0a multiset 7 What are multisets? Sets with (possibly) repeated elements Some operations: f#g :: 0a multiset add mset :: 0a ) 0a multiset ) 0a multiset + :: 0a multiset ) 0a multiset ) 0a multiset mset :: 0a list ) 0a multiset set mset :: 0a multiset ) 0a set Import HOL−Library:Multiset 8 1 Correctness 2 Insertion Sort 3 Time 4 Merge Sort 9 HOL/Data_Structures/Sorting.thy Insertion Sort Correctness 10 1 Correctness 2 Insertion Sort 3 Time 4 Merge Sort 11 Principle: Count function calls For every function f :: τ 1 ) ::: ) τ n ) τ define a timing function Tf :: τ 1 ) ::: ) τ n ) nat: Translation of defining equations: E[[f p1 ::: pn = e]] = (Tf p1 ::: pn = T [[e]] + 1) Translation of expressions: T [[g e1 ::: ek]] = T [[e1]] + ::: + T [[ek]] + Tg e1 ::: ek All other operations (variable access, constants, constructors, primitive operations on bool and numbers) cost 1 time unit 12 Example: @ E[[ [] @ ys = ys ]] = (T@ [] ys = T [[ys]] + 1) = (T@ [] ys = 2) E[[ (x # xs)@ ys = x #(xs @ ys) ]] = (T@ (x # xs) ys = T [[x #(xs @ ys)]] + 1) = (T@ (x # xs) ys = T@ xs ys + 5) T [[x #(xs @ ys)]] = T [[x]] + T [[xs @ ys]] + T# x (xs @ ys) = 1 + (T [[xs]] + T [[ys]] + T@ xs ys) + 1 = 1 + (1 + 1 + T@ xs ys) + 1 13 if and case So far we model a call-by-value semantics Conditionals and case expressions are evaluated lazily. T [[if b then e1 else e2]] = T [[b]] + (if b then T [[e1]] else T [[e2]]) T [[case e of p1 ) e1 j ::: j pk ) ek]] = T [[e]] + (case e of p1 )T [[e1]] j ::: j pk )T [[ek]]) Also special: let x = t1 in t2 14 O(:) is enough =) Reduce all additive constants to 1 Example T@ (x # xs) ys = T@ xs ys + 5 T@ (x # xs) ys = T@ xs ys + 1 This means we count only • the defined functions via Tf and • +1 for the function call itself. All other operations (variables etc) cost 0, not 1. 15 Discussion • The definition of Tf from f can be automated. • The correctness of Tf could be proved w.r.t. a semantics that counts computation steps. • Precise complexity bounds (as opposed to O(:)) would require a formal model of (at least) the compiler and the hardware. 16 HOL/Data_Structures/Sorting.thy Insertion sort complexity 17 1 Correctness 2 Insertion Sort 3 Time 4 Merge Sort 18 4 Merge Sort Top-Down Bottom-Up 19 merge :: 0a list ) 0a list ) 0a list merge [] ys = ys merge xs [] = xs merge (x # xs)(y # ys) = (if x ≤ y then x # merge xs (y # ys) else y # merge (x # xs) ys) msort :: 0a list ) 0a list msort xs = (let n = length xs in if n ≤ 1 then xs else merge (msort (take (n div 2) xs)) (msort (drop (n div 2) xs))) 20 ≤ length xs + length ys Number of comparisons C merge :: 0a list ) 0a list ) nat C msort :: 0a list ) nat Lemma C merge xs ys Theorem length xs = 2k =) C msort xs ≤ k ∗ 2k 21 HOL/Data_Structures/Sorting.thy Merge Sort 22 4 Merge Sort Top-Down Bottom-Up 23 msort bu :: 0a list ) 0a list msort bu xs = merge all (map (λx: [x]) xs) merge all :: 0a list list ) 0a list merge all [] = [] merge all [xs] = xs merge all xss = merge all (merge adj xss) merge adj :: 0a list list ) 0a list list merge adj [] = [] merge adj [xs] = [xs] merge adj (xs # ys # zss) = merge xs ys # merge adj zss 24 Number of comparisons C merge adj :: 0a list list ) nat C merge all :: 0a list list ) nat C msort bu :: 0a list ) nat Theorem length xs = 2k =) C msort bu xs ≤ k ∗ 2k 25 HOL/Data_Structures/Sorting.thy Bottom-Up Merge Sort 26 Even better Make use of already sorted subsequences Example Sorting [7; 3; 1; 2; 5]: do not start with [[7]; [3]; [1]; [2]; [5]] but with [[1; 3; 7]; [2; 5]] 27 Archive of Formal Proofs https://www.isa-afp.org/entries/ Efficient-Mergesort.shtml 28 Chapter 7 Binary Trees 29 5 Binary Trees 6 Basic Functions 7 (Almost) Complete Trees 30 5 Binary Trees 6 Basic Functions 7 (Almost) Complete Trees 31 HOL/Library/Tree.thy 32 Binary trees datatype 0a tree = Leaf j Node ( 0a tree) 0a ( 0a tree) hi ≡ Leaf Abbreviations: hl; a; ri ≡ Node l a r Most of the time: tree = binary tree 33 5 Binary Trees 6 Basic Functions 7 (Almost) Complete Trees 34 Tree traversal inorder :: 0a tree ) 0a list inorder hi = [] inorder hl; x; ri = inorder l @[x]@ inorder r preorder :: 0a tree ) 0a list preorder hi = [] preorder hl; x; ri = x # preorder l @ preorder r postorder :: 0a tree ) 0a list postorder hi = [] postorder hl; x; ri = postorder l @ postorder r @[x] 35 Size size :: 0a tree ) nat jhij = 0 jhl; ; rij = jlj + jrj + 1 size1 :: 0a tree ) nat jhij1 = 1 jhl; ; rij1 = jlj1 + jrj1 Lemma jtj1 = jtj + 1 Warning: j:j and j:j1 only on slides 36 Height height :: 0a tree ) nat h(hi) = 0 h(hl; ; ri) = max (h(l)) (h(r)) + 1 Warning: h(:) only on slides Lemma h(t) ≤ jtj h(t) Lemma jtj1 ≤ 2 37 Minimal height min height :: 0a tree ) nat mh(hi) = 0 mh(hl; ; ri) = min (mh(l)) (mh(r)) + 1 Warning: mh(:) only on slides Lemma mh(t) ≤ h(t) mh(t) Lemma 2 ≤ jtj1 38 5 Binary Trees 6 Basic Functions 7 (Almost) Complete Trees 39 Complete tree complete :: 0a tree ) bool complete hi = True complete hl; ; ri = (h(l) = h(r) ^ complete l ^ complete r) Lemma complete t = (mh(t) = h(t)) h(t) Lemma complete t =) jtj1 = 2 h(t) Lemma : complete t =) jtj1 < 2 mh(t) Lemma : complete t =) 2 < jtj1 h(t) Corollary jtj1 = 2 =) complete t mh(t) Corollary jtj1 = 2 =) complete t 40 Almost complete tree acomplete :: 0a tree ) bool acomplete t = (h(t) − mh(t) ≤ 1) Almost complete trees have optimal height: Lemma If acomplete t and jtj ≤ jt 0j then h(t) ≤ h(t 0): 41 Warning • The terms complete and almost complete are not defined uniquely in the literature. • For example, Knuth calls complete what we call almost complete. 42 Chapter 8 Search Trees 43 8 Unbalanced BST 9 Abstract Data Types 10 2-3 Trees 11 Red-Black Trees 12 More Search Trees 13 Union, Intersection, Difference on BSTs 14 Tries and Patricia Tries 44 Most of the material focuses on BSTs = binary search trees 45 BSTs represent sets Any tree represents a set: set tree :: 0a tree ) 0a set set tree hi = fg set tree hl; x; ri = set tree l [ fxg [ set tree r A BST represents a set that can be searched in time O(h(t)) Function set tree is called an abstraction function because it maps the implementation to the abstract mathematical object 46 bst bst :: 0a tree ) bool bst hi = True bst hl; a; ri = ((8 x2set tree l: x < a) ^ (8 x2set tree r: a < x) ^ bst l ^ bst r) Type 0a must be in class linorder ( 0a :: linorder) where linorder are linear orders (also called total orders). Note: nat, int and real are in class linorder 47 Set interface An implementation of sets of elements of type 0a must provide • An implementation type 0s • empty :: 0s • insert :: 0a ) 0s ) 0s • delete :: 0a ) 0s ) 0s • isin :: 0s ) 0a ) bool 48 Map interface Instead of a set, a search tree can also implement a map from 0a to 0b: • An implementation type 0m • empty :: 0m • update :: 0a ) 0b ) 0m ) 0m • delete :: 0a ) 0m ) 0m • lookup :: 0m ) 0a ) 0b option Sets are a special case of maps 49 Comparison of elements We assume that the element type 0a is a linear order Instead of using < and ≤ directly: datatype cmp val = LT j EQ j GT cmp x y = (if x < y then LT else if x = y then EQ else GT) 50 8 Unbalanced BST 9 Abstract Data Types 10 2-3 Trees 11 Red-Black Trees 12 More Search Trees 13 Union, Intersection, Difference on BSTs 14 Tries and Patricia Tries 51 8 Unbalanced BST Implementation Correctness Correctness Proof Method Based on Sorted Lists 52 Implementation type: 0a tree empty = Leaf insert x hi = hhi; x; hii insert x hl; a; ri = (case cmp x a of LT ) hinsert x l; a; ri j EQ ) hl; a; ri j GT ) hl; a; insert x ri) 53 isin hi x = False isin hl; a; ri x = (case cmp x a of LT ) isin l x j EQ ) True j GT ) isin r x) 54 delete x hi = hi delete x hl; a; ri = (case cmp x a of LT ) hdelete x l; a; ri j EQ ) if r = hi then l else let (a 0; r 0) = split min r in hl; a 0; r 0i j GT ) hl; a; delete x ri) split min hl; a; ri = (if l = hi then (a; r) else let (x; l 0) = split min l in (x; hl 0; a; ri)) 55 8 Unbalanced BST Implementation Correctness Correctness Proof Method Based on Sorted Lists 56 Why is this implementation correct? Because empty insert delete isin simulate fg [ f:g − f:g 2 set tree empty = fg set tree (insert x t) = set tree t [ fxg set tree (delete x t) = set tree t − fxg isin t x = (x 2 set tree t) Under the assumption bst t 57 Also: bst must be invariant bst empty bst t =) bst (insert x t) bst t =) bst (delete x t) 58 8 Unbalanced BST Implementation Correctness Correctness Proof Method Based on Sorted Lists 59 Key idea Local definition: sorted means sorted w.r.t.
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