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Dec 23, 2023 •347 likes •1.41k views

Dynamic programming Life can only be understood backwards; but it must be lived forwards." Soren Kierkegaard Dynamic programming An interesting question is, "Where did the name, dynamic programming, come from?" The 1950's

  1. The coin change problem • So far we computed how many coins • Now want to know which values, as in 8 = 4+4 • Alg2(t): { Auxiliary arrays C[0..t], A[0..t] C[ 0 ] = 0; A[ 0 ] = 0 For (s = 1..t) { m = minimum of C[s - d i ] over i = 1..k such that s – d i ≥ 0 i = arg-minimum C[s] = 1 + m A[s] = d i } • Idea: values are: A[t], A[t-A[t] ], … . until you get zero

  2. The coin change problem • Printing the coins used • Print-Coins(t) { for(i = t; i > 0; i = i – A[i] ) Print(A[i]) } • Time O(t)

  3. The coin change problem • Example: k = 3, d = (5,4,1), t = 8 0 1 2 3 4 5 6 7 8 C 0 1 2 3 1 1 2 3 2 A 0 1 1 1 4 5 5 5 4 Print-coins(8) = 4, 4

  4. The coin change problem • Example: k = 3, d = (5,4,1), t = 8 0 1 2 3 4 5 6 7 8 C 0 1 2 3 1 1 2 3 2 A 0 1 1 1 4 5 5 5 4 Print-coins(7) = 5, 1, 1

  5. Steps for dynamic programming • Identify subproblems (In coin-change example Cost[1..t]) • Obtain recursion (Cost[t] = 1 + min i ≤ k Cost[t - d i ] ) (aka structure of solutions, optimal substructure property) • Algorithm solves all the subproblems, once • Running time = Number of subproblems (here t ) x Time to compute recursion (here O(k) )

  6. ● Saw dynamic programming as iterative, “ bottom-up ” : solve all the problems from the smallest to the biggest. ● Can also be implemented in a “ top-down ” recursive fashion: Keep a list of the subproblems solved, and at the beginning you check if the current subproblem was already solved, if so you just read off the solution and return. ● This is called Memoization ● Recall even divide-and-conquer may be implemented either in a recursive “ top-down ” fashion, or in an iterative “ bottom-up ” fashion.

  7. Longest common subsequence ● Given two strings X and Y over some alphabet, want to find a longest subsequence Z. The symbols in Z appear in X, Y in the same order, but not necessarily consecutively ● Example: Alphabet = {A, C, G, T} X = A A G G A C A C T C T A G C G A T Y = T G G C A T T T A C G C G C A A

  8. Longest common subsequence ● Given two strings X and Y over some alphabet, want to find a longest subsequence Z. The symbols in Z appear in X, Y in the same order, but not necessarily consecutively ● Example: Alphabet = {A, C, G, T} X = A A G G A C A C T C T A G C G A T Y = T G G C A T T T A C G C G C A A Z = G A T T A C A

  9. Longest common subsequence • Arriving at subproblems and recursion X = A A G G A C A C T C T A G C G A T Y = T G G C A T T T A C G C G C A A The strings X and Y end with different symbols. So either last T in X is not part of the solution, or last A in Y is not part of the solution. In the first case I can remove last T from X Now both strings end with A, which can be matched. In the latter case I can remove the last A from Y.

  10. Longest common subsequence • On input X[1..m], Y[1..n], consider the prefixes X[1..i], Y[1..j] for any i ≤ m, j ≤ n. • Subproblems: L(i,j) = length longest subsequence of X[1..i] and Y[1..j] • Recursion: if i = 0 or j = 0 L(i,j) = 0 if X[i] = Y[j] L(i,j) = ? if X[i] ≠ Y[j] L(i,j) = ?

  11. Longest common subsequence • On input X[1..m], Y[1..n], consider the prefixes X[1..i], Y[1..j] for any i ≤ m, j ≤ n. • Subproblems: L(i,j) = length longest subsequence of X[1..i] and Y[1..j] • Recursion: if i = 0 or j = 0 L(i,j) = 0 if X[i] = Y[j] L(i,j) = L(i-1,j-1) + 1 if X[i] ≠ Y[j] L(i,j) = ?

  12. Longest common subsequence • On input X[1..m], Y[1..n], consider the prefixes X[1..i], Y[1..j] for any i ≤ m, j ≤ n. • Subproblems: L(i,j) = length longest subsequence of X[1..i] and Y[1..j] • Recursion: if i = 0 or j = 0 L(i,j) = 0 if X[i] = Y[j] L(i,j) = L(i-1,j-1) + 1 if X[i] ≠ Y[j] L(i,j) = max {L(i-1,j), L(i,j-1) }

  13. ● LCSLength(X[1..m], Y[1..n]) 0 1 2 3 4 5 6 7 L = zero array(0..m, 0..n) Ø M Z J A W C U 0 Ø 0 0 0 0 0 0 0 0 1 C 0 0 0 0 0 0 1 1 for i := 1..m 2 M 0 1 1 1 1 1 1 1 3 J 0 1 1 2 2 2 2 2 for j := 1..n 4 T 0 1 1 2 2 2 2 2 if X[i] = Y[j] 5 A 0 1 1 2 3 3 3 3 6 U 0 1 1 2 3 3 3 4 L[i,j] := L[i-1,j-1] + 1 7 Z 0 1 2 2 3 3 3 4 else L[i,j] := max(L[i,j-1], L[i-1,j]) return L[m,n] ● Running time = O(mn)

  14. Longest common subsequence • If we want to output the sequence, we record which rule was used at each point ↖ if the last symbols match 0 1 2 3 4 5 6 7 ← if we are dropping last symbol of X Ø M Z J A W C U ↑ if we are dropping last symbol of Y 0 Ø 0 0 0 0 0 0 0 0 1 C 0 0 0 0 0 0 1 1 2 M 0 1 1 1 1 1 1 1 Then we can reconstruct 3 J 0 1 1 2 2 2 2 2 the sequence backwards. 4 T 0 1 1 2 2 2 2 2 5 A 0 1 1 2 3 3 3 3 6 U 0 1 1 2 3 3 3 4 7 Z 0 1 2 2 3 3 3 4

  15. Dynamic programming in economics ● Let us plan Bob's next L years. ● He has $w, and every year makes $w ● At the beginning of each year, he must decide how much to consume, rest is saved Savings earn interest (1+ρ) (round to integer) Consuming C yields utility log(C) ($10K vs. $20K is different from $1M vs. $1M+$10K) ● He wants to maximize sum of utility throughout his lifetime

  16. Life can only be understood backwards; but it must be lived forwards. Soren Kierkegaard

  17. ● Subproblems and recursion ● U[k,i] := utility for years i, i+1, …, L if at beginning of year i have $k. Note k integer ≤ M := wL (1 + ρ ) L ● U[k,L] := ? How much should Bob consume in his last year of life?

  18. Subproblems and recursion ● ● U[k,i] := utility for years i, i+1, … , L if at beginning of year i have $k. Note k integer ≤ M := wL (1 + ρ ) L ● U[k,L] := log(k) Consumption = k, because at last year L he spends all ● U[k,i] := What recursion for i < L ?

  19. Subproblems and recursion ● ● U[k,i] := utility for years i, i+1, … , L if at beginning of year i have $k. Note k integer ≤ M := wL (1 + ρ ) L ● U[k,L] := log(k) Consumption = k, because at last year L he spends all ● U[k,i] := max c : 0 ≤ c ≤ M log (c) + U[(k - c)(1+ρ) + w, i+1] Consumption = argmax ● ⇒ Dynamic programming algorithm running in time O(LM 2 )

  20. ● Slightly more realism ● With probability q Bob earns interest rate (1 +ρ) ● With probability 1-q Bob loses money rate (1- ρ ) ● U[k,i] := expected utility for years i, i+1, … , L if at beginning of year i has $k ● U[k,L] := log(k) ● U[k,i] := max c: 0 ≤ c ≤ M log (c) +?

  21. ● Slightly more realism ● With probability q Bob earns interest rate (1 +ρ) ● With probability 1-q Bob loses money rate (1- ρ ) ● U[k,i] := expected utility for years i, i+1, … , L if at beginning of year i has $k ● U[k,L] := log(k) ● U[k,i] := max c: 0 ≤ c ≤ M log (c)+ q U[(k - c)(1 +ρ) + w, i+1] + (1-q) U[(k - c)(1- ρ) + w, i+1]

  22. Subset sum problem Problem: Input integers w 1 , w 2 … , w n , t ● Output: Number of (subsets) x ∈ {0,1} n : σ 𝑗=1 𝑜 𝑥 𝑗 ⋅ 𝑦 𝑗 = 𝑢 ● Example: n = 3, t = 12 w = {2, 3, 5, 7, 10} t = 10+2, 7+5, 7+3+2 Output = 3

  23. Subset sum problem Problem: Input integers w 1 , w 2 … , w n , t ● Output: Number of (subsets) x ∈ {0,1} n : σ 𝑗=1 𝑜 𝑥 𝑗 ⋅ 𝑦 𝑗 = 𝑢 ● Arriving at subproblems and recursion To get to t we can either: use w n then need to get to t - w n using w 1 , w 2 … , w n-1 or not then need to get to t using w 1 , w 2 … , w n-1

  24. Subset sum problem Problem: Input integers w 1 , w 2 … , w n , t ● Output: Number of (subsets) x ∈ {0,1} n : σ 𝑗=1 𝑜 𝑥 𝑗 ⋅ 𝑦 𝑗 = 𝑢 ● Subproblems and recursion: ● S(i,s) := number of x ∈ {0,1} i such that σ 𝑘=1 𝑗 𝑥 𝑘 ⋅ 𝑦 𝑘 = 𝑡 ● Recursion: S(i,s) = S(i-1,s) + S(i-1,s – w i ) ● ● There are only tn different subproblems S(i,s) (Don ’ t need to consider sums larger than t) NOTE: Assuming weights are positive: 𝑥 𝑗 ≥ 0 for all 𝑗

  25. Problem: Input integers w 1 , w 2 … , w n , t ● Output: Number of (subsets) x ∈ {0,1} n : σ 𝑗=1 𝑜 𝑥 𝑗 ⋅ 𝑦 𝑗 = 𝑢 ● 1 Sum s ,,, 1 2 i = 1...n 1 1 Fill first column ● (for i = 2... n) ● Algorithm (for s = 0 … t) ?

  26. Problem: Input integers w 1 , w 2 … , w n , t ● Output: Number of (subsets) x ∈ {0,1} n : σ 𝑗=1 𝑜 𝑥 𝑗 ⋅ 𝑦 𝑗 = 𝑢 ● 1 Sum s ,,, 1 2 i = 1...n 1 1 Fill first column ● (for i = 2... n) ● Algorithm (for s = 0 … t) S(i,s) = S(i-1,s) + S(i-1,s – w i ) T(n) = ? ●

  27. Problem: Input integers w 1 , w 2 … , w n , t ● Output: Number of (subsets) x ∈ {0,1} n : σ 𝑗=1 𝑜 𝑥 𝑗 ⋅ 𝑦 𝑗 = 𝑢 ● 1 Sum s ,,, 1 2 i = 1...n 1 1 Fill first column ● (for i = 2... n) ● (for s = 0 … t) S(i,s) = S(i-1,s) + S(i-1,s – w i ) T(n) = O(tn) ●

  28. Problem: Input integers w 1 , w 2 … , w n , t ● Output: Number of (subsets) x ∈ {0,1} n : σ 𝑗=1 𝑜 𝑥 𝑗 ⋅ 𝑦 𝑗 = 𝑢 ● 1 Sum s ,,, 1 2 i = 1...n 1 1 Fill first column ● (for i = 2... n) ● (for s = 0 … t) S(i,s) = S(i-1,s) + S(i-1,s – w i ) Space: Trivial: O(tn) Better: ?? ●

  29. Problem: Input integers w 1 , w 2 … , w n , t ● Output: Number of (subsets) x ∈ {0,1} n : σ 𝑗=1 𝑜 𝑥 𝑗 ⋅ 𝑦 𝑗 = 𝑢 ● 1 Sum s ,,, 1 2 i = 1...n 1 1 Fill first column ● (for i = 2... n) ● (for s = 0 … t) S(i,s) = S(i-1,s) + S(i-1,s – w i ) Space: O(t), just keep two columns ●

  30. 2 3 12 Example: 11 n = 3, t = 12 1 2 3 10 w = {2, 3, 5, 7, 10} 1 1 t = 10+2, 7+5, 7+3+2 9 1 1 1 8 Output = 3 1 2 2 7 6 1 2 2 2 5 4 1 1 1 1 3 1 1 1 1 1 2 1 1 1 1 1 1 0 1 2 3 4 5

  31. Greedy Algorithms Dynamic programming requires solving all subproblems, leads to algorithms with running time usually n 2 or n 3 Sometimes, greedy is faster. A greedy algorithm always makes the choice that looks best at the moment. That is, it keeps making locally optimal decision in the hope that this will lead to a globally optimal solution.

  32. Activity Selection problem Input: Set of n activities that need the same resource. 𝐵 ≔ 𝑏 1 , 𝑏 2 , … 𝑏 𝑜 Activity a i takes time [s i , f i ). Activities a i , a j are compatible if 𝑡 𝑘 ≥ 𝑔 𝑗 Output: Maximum-size subset of mutually compatible activities.

  33. Example: a i 1 2 3 4 5 6 7 8 9 10 11 s i 1 3 0 5 3 5 6 8 8 2 12 f 4 5 6 7 8 9 10 11 12 13 14 i A set of compatible activities = ?

  34. Example: a i 1 2 3 4 5 6 7 8 9 10 11 s i 1 3 0 5 3 5 6 8 8 2 12 f 4 5 6 7 8 9 10 11 12 13 14 i A set of compatible activities = (a ,a ,a ). 3 9 11

  35. Example: a i 1 2 3 4 5 6 7 8 9 10 11 s i 1 3 0 5 3 5 6 8 8 2 12 f 4 5 6 7 8 9 10 11 12 13 14 i A set of compatible activities = (a ,a ,a ). 3 9 11 A maximal set of compatible activities = ?

  36. Example: a i 1 2 3 4 5 6 7 8 9 10 11 s i 1 3 0 5 3 5 6 8 8 2 12 f 4 5 6 7 8 9 10 11 12 13 14 i A set of compatible activities = (a ,a ,a ). 3 9 11 A maximal set of compatible activities = (a1,a4,a8,a11)

  37. Example: a i 1 2 3 4 5 6 7 8 9 10 11 s i 1 3 0 5 3 5 6 8 8 2 12 f 4 5 6 7 8 9 10 11 12 13 14 i A set of compatible activities = (a ,a ,a ). 3 9 11 A maximal set of compatible activities = (a1,a4,a8,a11) Is there another maximal set ?

  38. Example: a i 1 2 3 4 5 6 7 8 9 10 11 s i 1 3 0 5 3 5 6 8 8 2 12 f 4 5 6 7 8 9 10 11 12 13 14 i A set of compatible activities = (a ,a ,a ). 3 9 11 A maximal set of compatible activities = (a1,a4,a8,a11) Is there another maximal set? Yes. (a2,a4,a9,a11)

  39. ● Claim: some optimal solution contains activity with earliest finish time ● Proof: Let [s*,f*) be activity with earliest finish time f* Let S be an optimal solution Write S = S' U [s,f) where [s,f) has earliest finish time among activities in S ● Then S' U [s*,f*) is also an optimal solution, because every activity in S' has start time > f > f*.

  40. ● Greedy Algorithm: Pick activity with earliest finish time, that does not overlap with activities already picked Repeat Claim: The algorithm is correct ● Proof: Follows from applying previous claim iteratively. ● ● Let us see the algorithm in more detail

  41. Greedy activity selection algorithm activity-selection(A) { sort A increasingly according to f [i]; n:= length[A]; S:=a[1] i:=1; for (m=2; m ≤ n; m++) if (s[m] ≥ f[i] ) { Add a[i] to S; i :=m; } return S }

  42. Example: activity-selection(A) { sort A increasingly according to f [i]; n:= length[A]; S:=a[1] i:=1; for (m=2; m ≤ n; m++) i a i 2 3 4 5 6 7 8 9 10 11 1 if (s[m] ≥ f[i] ) { i s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 }

  43. Example: activity-selection(A) { Already sorted sort A increasingly according to f [i]; according to finish time. n:= length[A]; S:= a[1] i:=1; for (m=2; m ≤ n; m++) i a i 2 3 4 5 6 7 8 9 10 11 1 if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 }

  44. Example: activity-selection(A) { sort A increasingly according to f [i]; n:= length[A]; n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 }

  45. Example: activity-selection(A) { sort A increasingly S:={a } 1 according to f [i]; n:= length[A]; n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 }

  46. Example: activity-selection(A) { sort A increasingly S:={a } 1 according to f [i]; n:= length[A]; S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 }

  47. Example: activity-selection(A) { sort A increasingly S:={a } 1 according to f [i]; n:= length[A]; i m n:=11 S:= a[1] i:=1; for (m=2 ; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 }

  48. Example: activity-selection(A) { sort A increasingly S:={a } 1 according to f [i]; s[2] ≥ f[1] ? n:= length[A]; i m n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 }

  49. Example: activity-selection(A) { sort A increasingly S:={a } 1 according to f [i]; s[2] < f[1] n:= length[A]; i m n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  50. Example: activity-selection(A) { sort A increasingly S:={a } 1 according to f [i]; s[3] ≥ f[1] ? n:= length[A]; i m n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  51. Example: activity-selection(A) { sort A increasingly S:={a } 1 according to f [i]; s[3] < f[1] n:= length[A]; i m n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  52. Example: activity-selection(A) { sort A increasingly S:={a } 1 according to f [i]; s[4] ≥ f[1] ? n:= length[A]; i m n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  53. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a } 4 according to f [i]; s[4] > f[1] n:= length[A]; i m n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  54. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a } 4 according to f [i]; s[4] > f[1] n:= length[A]; i,m n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  55. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a } 4 according to f [i]; n:= length[A]; i m n:=11 S:= a[1] i:=1; a for (m=2; m ≤ n; m++) 1 2 3 4 5 6 7 8 9 10 11 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i f i :=m;} return S; 4 5 6 7 8 9 10 11 12 13 14 i }

  56. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a } 4 according to f [i]; s[5] ≥ f[4] ? n:= length[A]; i m n:=11 S:= a[1] i:=1; a for (m=2; m ≤ n; m++) 1 2 3 4 5 6 7 8 9 10 11 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i f i :=m;} return S; 4 5 6 7 8 9 10 11 12 13 14 i }

  57. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a } 4 according to f [i]; s[5] < f[4] n:= length[A]; i m n:=11 S:= a[1] i:=1; a for (m=2; m ≤ n; m++) 1 2 3 4 5 6 7 8 9 10 11 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i f i :=m;} return S; 4 5 6 7 8 9 10 11 12 13 14 i }

  58. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a } 4 according to f [i]; s[6] ≥ f[4] ? n:= length[A]; i m n:=11 S:= a[1] i:=1; a for (m=2; m ≤ n; m++) 1 2 3 4 5 6 7 8 9 10 11 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i f i :=m;} return S; 4 5 6 7 8 9 10 11 12 13 14 i }

  59. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a } 4 according to f [i]; s[6] < f[4] n:= length[A]; i m n:=11 S:= a[1] i:=1; a for (m=2; m ≤ n; m++) 1 2 3 4 5 6 7 8 9 10 11 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i f i :=m;} return S; 4 5 6 7 8 9 10 11 12 13 14 i }

  60. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a } 4 according to f [i]; s[7] ≥ f[4] ? n:= length[A]; i m n:=11 S:= a[1] i:=1; a for (m=2; m ≤ n; m++) 1 2 3 4 5 6 7 8 9 10 11 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i f i :=m;} return S; 4 5 6 7 8 9 10 11 12 13 14 i }

  61. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a } 4 according to f [i]; s[7] < f[4] n:= length[A]; i m n:=11 S:= a[1] i:=1; a for (m=2; m ≤ n; m++) 1 2 3 4 5 6 7 8 9 10 11 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i f i :=m;} return S; 4 5 6 7 8 9 10 11 12 13 14 i }

  62. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a } 4 according to f [i]; s[8] ≥ f[4] ? n:= length[A]; i m n:=11 S:= a[1] i:=1; a for (m=2; m ≤ n; m++) 1 2 3 4 5 6 7 8 9 10 11 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i f i :=m;} return S; 4 5 6 7 8 9 10 11 12 13 14 i }

  63. Example: activity-selection(A) { S:={a 1 ,a 4 ,a } sort A increasingly 8 according to f [i]; s[8] > f[4] n:= length[A]; i m n:=11 S:= a[1] i:=1; a for (m=2; m ≤ n; m++) 1 2 3 4 5 6 7 8 9 10 11 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i f i :=m;} return S; 4 5 6 7 8 9 10 11 12 13 14 i }

  64. Example: activity-selection(A) { S:={a 1 ,a 4 ,a } sort A increasingly 8 according to f [i]; s[8] > f[4] n:= length[A]; i,m n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  65. Example: activity-selection(A) { S:={a 1 ,a 4 ,a } sort A increasingly 8 according to f [i]; n:= length[A]; i m n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  66. Example: activity-selection(A) { S:={a 1 ,a 4 ,a } sort A increasingly 8 according to f [i]; s[9] ≥ f[8] ? n:= length[A]; i m n:=11 S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  67. Example: activity-selection(A) { S:={a 1 ,a 4 ,a } sort A increasingly 8 according to f [i]; s[9] < f[8] n:= length[A]; i m S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  68. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a 4 ,a } 8 according to f [i]; s[10] ≥ f[8] ? n:= length[A]; i m S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  69. Example: activity-selection(A) { S:={a 1 ,a 4 ,a } sort A increasingly 8 according to f [i]; s[10] < f[8] n:= length[A]; i m S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  70. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a 4 ,a } 8 according to f [i]; S[11] ≥ f[8] ? n:= length[A]; i m S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  71. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a 4 ,a 8 ,a } 11 according to f [i]; s[11] ≥ f[8] n:= length[A]; i m S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 }

  72. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a 4 ,a 8 ,a 11 } according to f [i]; s[11] ≥ f[8] n:= length[A]; i,m S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 }

  73. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a 4 ,a 8 ,a 11 } m = 12, according to f [i]; n:= length[A]; n = 11 i S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 }

  74. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a 4 ,a 8 ,a 11 } m = 12, according to f [i]; n = 11 n:= length[A]; i S:= a[1] i:=1; for (m=2; m ≤ n ; m++) a i 2 3 4 5 6 7 8 9 10 11 1 if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 }

  75. Example: activity-selection(A) { sort A increasingly S:={a 1 ,a 4 ,a 8 ,a 11 } according to f [i]; n:= length[A]; S:= a[1] i:=1; for (m=2; m ≤ n; m++) a i 2 3 4 5 6 7 8 9 10 11 1 i if (s[m] ≥ f[i] ) { s 2 12 1 3 0 5 3 5 6 8 8 Add a[i] to S; i i i :=m;} return S; f i 4 5 6 7 8 9 10 11 12 13 14 i }

  76. Deleted scenes

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