algorithms design & analysis - fudan universityliy/courses/alg/slides/alg-08.pdf · • example...

Algorithms Design & Analysis

Dynamic Programming

2

Recap

•  Divide-and-conquer design paradigm

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Today’s topics •  Dynamic programming

– Design paradigm – Assembly-line scheduling – Matrix-chain multiplication – Elements

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Decision Problem

•  Definition The answer of the problem is simply “yes” or “no”.

•  Example – Binary search – Reachability

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Optimization Problem

•  Definition Each feasible solution has an associated value, and we wish to find the feasible solution with the best value.

•  Example – Shortest path – Closest-pair problem

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a1,1 a1,2 a1,3 a1,4 a1,n-1 a1,n

a2,1 a2,2 a2,3 a2,4 a2,n-1 a2,n

t1,1 t1,n-1 t1,2 t1,3 e1

t2,1 t2,n-1 t2,2 t2,3 e2

x1

x2 …

chassis enters

completed auto exits

line 2

line 1

station S2,1

station S2,2

station S2,3

station S2,4

station S2,n-1

station S2,n

station S1,1

station S1,2

station S1,3

station S1,4

station S1,n-1

station S1,n

Assembly-line scheduling •  Manufacturing problem

ai,j: assembly time ti,j: transfer time ei: enter time xi: exit time

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Example

7 9 3 4 8 4

8 5 6 4 5 7

2 4 3 1 2

2 1 1 2 4

3

2

chassis enters

completed auto exits

line 2

line 1

station S2,1

station S2,2

station S2,3

station S2,4

station S2,5

station S2,6

station S1,1

station S1,2

station S1,3

station S1,4

station S1,5

station S1,6

3

2

37 30 25 22 16 12 f2[j]

35 32 24 20 18 9 f1[j]

6 5 4 3 2 1 j

2 2 1 2 1 l2[j]

2 1 1 2 1 l1[j]

6 5 4 3 2 j

f * = 38 l * = 1

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Brute-force algorithm

Brute-force algorithm: Exhaustively checking all possible lists.

running time = Ω(2n) It is too unlucky!

IDEA: If a list is given of which stations to use in line 1 and which to use in line 2, it is easy to compute in Θ(n) time how long it takes to pass through the factory.

There are 2 choices for each station!

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Optimal structure

Observation: The fastest way through station S1,j is either 1.  The fastest way through station S1,j-1 and then

directly through station S1,j. 2.  The fastest way through station S2,j-1, a transfer

from line 2 to line 1, and then through station S1,j.

•  To find the fastest way through station j of either line, we solve the subproblem of finding the fastest ways through station j - 1 on both lines.

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Recursive solution

•  For the first station, f1[1] = e1 + a1,1, f2[1] = e2 + a2,1

•  For the final station, f* = min(f1[n] + x1, f2[n] + x2)

•  The recurrence

{⎩⎨⎧

≥++−+−

=+=

− .2 if }]1[,]1[min.1 if

][,11,22,11

1,111 jatjfajf

jaejf

jjj

{⎩⎨⎧

≥++−+−

=+=

− .2 if }]1[,]1[min.1 if

][,21,11,22

1,222 jatjfajf

jaejf

jjj

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Find the fastest way FASTEST-WAY(a, t, e, x, n) f1[1] ← e1 + a1,1, f2[1] ← e2 + a2,1

for j ← 2 to n do if f1[j-1] + a1,j ≤ f1[j-1] +t2,j-1 + a1,j

then f1[j] ← f1[j-1] + a1,j; l1[j] ← 1 else f1[j] ← f2[j-1] +t2,j-1 + a1,j; l1[j] ← 2 if f2[j-1] + a2,j ≤ f2[j-1] +t1,j-1 + a2,j

then f2[j] ← f2[j-1] + a2,j ; l2[j] ← 2 else f2[j] ← f1[j-1] +t1,j-1 + a2,j; l2[j] ← 1

if f1[n] + x1, ≤ f2[n] + x2 then f * ← f1[n] + x1 l* ← 1 else f* ← f2[n] + x2

l * ← 2

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Example

7 9 3 4 8 4

8 5 6 4 5 7

2 4 3 1 2

2 1 1 2 4

3

2

chassis enters

completed auto exits

line 2

line 1

station S2,1

station S2,2

station S2,3

station S2,4

station S2,5

station S2,6

station S1,1

station S1,2

station S1,3

station S1,4

station S1,5

station S1,6

3

2

37 30 25 22 16 12 f2[j]

35 32 24 20 18 9 f1[j]

6 5 4 3 2 1 j

2 2 1 2 1 l2[j]

2 1 1 2 1 l1[j]

6 5 4 3 2 j

f * = 38 l * = 1

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Design paradigm

•  Characterize the structure of an optimal solution.

•  Define the value of an optimal solution recursively.

•  Compute the value of an optimal solution in a bottom-up fashion.

•  Construct an optimal solution from computed information.

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Matrix-chain multiplication

Input: a sequence of n matrices A1, A2, …, An

Output: a matrix B = A1×A2×…×An

Example: Bm,s = Am,n×An,o×…×Ar,s

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Multiplication of two matrices MATRIX-MULTIPLY(A,B)

if columns[A] ≠ rows[B] then error "incompatible dimensions" else for i ← 1 to rows[A] do for j ← 1 to columns[B] do C[i, j] ← 0

for k ← 1 to columns[A] do C[i ,j] ← C[i ,j] + A [i, k] · B[k, j]

return C

Suppose A is a p×q matrix, B is a q×r matrix Running time = Θ(p×q×r)

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Naïve algorithm

Naïve Algorithm: Multiply the matrices from left to right.

•  Suppose the size of the n matrice are p1×p2, p2×p3 ,…, pn-1×pn

•  Generalization of two matrice’s case Running time = Ο(p1×p2×…×pn)

Any better way to reduce the numbers of scalar multiplication???

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Matrix-chain: Example •  4 matrices: A1×A2×A3×A4

A1: 15×5 A2: 5×10 A3: 10×20 A4: 20×25

Associative law: (A1×A2)×A3 = A1×(A2×A3)

((A1×A2)×A3)×A4 : 15·5·10+15·10·20+15·20·25=11250 (A1×A2)×(A3×A4) : 15·5·10+10·20·15+15·10·25=13250 (A1×(A2×A3))×A4 : 5·10·20+15·5·20+15·20·25=10000 A1×((A2×A3)×A4) : 5·10·20+5·20·25+15·5·25=5375 A1×(A2×(A3×A4)) : 10·20·25+15·10·25+15·5·25=8125

Minimal number of multiplications!

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Brute-force algorithm

Brute-force algorithm: Exhausitively checking all possible parenthesization.

What’s the running time?

IDEA: Find a parenthesization that minimizes the number of scalar multiplications for n matrices < A1, …, An> with dimensions pi-1×pi, for 1 ≤ i ≤ n.

Depends on the number of parenthesization!

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Number of parenthesization •  The recurrence of the number of parenthesization

–  For a single matrix, we have only one. –  For a sequence of n matrices, split it between the kth and

(k+1)st matrices and parenthesize the subsequences recursively.

⎪⎩

⎪⎨⎧

≥−

== ∑

−

=

1

1.2 if )()(.1 if 1

)( n

knknPkPn

nP

)4(2

11)(),1()( 2/3nn

nn

nCnCnPn

Ω=⎟⎟⎠

⎞⎜⎜⎝

⎛

+=−=

Unlucky! It is exponential in n…

C(n) is the Catalan number!

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More clever way… Notation: Ai…j means Ai Ai+1…Aj , where i ≤ j

•  Given Ai…j with an optimal parenthesization There must be a i ≤ k < j such that Ai…j = (Ai…k)·(Ak+1…j).

•  Cost of computing Ai…j – Cost of computing Ai…k and Ak+1…j

– Cost of computing (Ai…k)·(Ak+1…j)

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More clever way…(Cont.)

Assertion: Suppose that an optimal parethesization of Ai…j is split at k. Then the parethesization subchain Ai…k within this optimization of Ai…j must be an optimal parenthesization of Ai…k.

Why?

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Recursive solution Notation: m[i,j] be the minimum number of scalar multiplications to compute Ai…j

•  If there is one matrix Ai: m[i,i] = 0 •  Suppose optimal parenthesization of Ai…j is k

m[i,j] = m[i,k] + m[k+1,j] + pi-1pkpj

•  Finally,

m[i, j]=0 if i = j.mini≤k< j

m[i, j]=m[i,k]+m[k +1, j]+ pi−1pk pj} if i < j.{

#

$%

&%

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Recursive Algorithm RECURSIVE-MATRIX-CHAIN(p,i,j)

if i = j then return 0 m[i, j] ← ∞ for k ← i to j - 1 //computing the minimum cost do q ← RECURSIVE-MATRIX-CHAIN(p, i, k) + RECURSIVE-MATRIX-CHAIN(p, k + 1, j)

+ pi-1pkpj if q < m[i, j] then m[i, j] ← q return m[i, j]

p = <p1, p2,.., pn >

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The great moment…

•  The recurrence

Theorem. The running time satisfies

T(n) ≥ 2n-1

Exponetial time, it is too bad!

∑−

=

>+−++≥

≥1

1.1for )1)()((1)(

,1)1(n

knknTkTnT

T

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The great moment…(Cont.) Proof.(Induction)

The base, T(1)≥1 = 20. Suppose T(n-1) ≥ 2n-2.

when i = n, we have

∑−

=

+≥1

1

)(2)(n

iniTnT

∑−

=

− +=1

1

122n

i

i n

∑−

=

+=2

0

22n

i

i n

11 222)12(2 −− ≥−+=+−= nnn nn

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It is totally frustrated!

We need to find another better way!

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Bottom-up method MATRIX-CHAIN-ORDER(p)

n ← length[p] – 1 for i ← 1 to n do m[i, i] ← 0 // initialize the entries for l ← 2 to n // l is the chain length do for i ← 1 to n – l + 1

do j ← i + l – 1 // j is i + length of chain -1 m[i, j] ← ∞ for k ← i to j – 1 //based on previous result

do q ← m[i, k] + m[k + 1, j] + pi–1 pk pj if q < m[i, j] // choose the minimal

then m[i, j] ← q s[i, j] ← k

return m and s

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The truly great moment…

•  Correctness? •  Space complexity

T(n) = Θ(n2) – m[1…n, 1…n]: store the cost m[i,j] –  s[1…n,1…n]: records the index k achieved

optimal cost in computing m[i,j] •  Time complexity

T(n) = Θ(n3)

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Example •  Given matrices

A1: 30×35 A2: 35×15 A3: 15×5 A4: 5×10 A5: 10×20 A6: 20×25

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Example(Cont.) •  Computing of m[2,5]

7125

.1137520103504375]5,5[]4,2[.71252053510002625]5,4[]3,2[

.1300020153525000]5,3[]2,2[min]5,2[

541

531

521

=

⎪⎩

⎪⎨

⎧

=⋅⋅++=++

=⋅⋅++=++

=⋅⋅++=++

=

pppmmpppmmpppmm

m

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Constructing the optimal solution

MATRIX-CHAIN-MULTIPLY(A, s, i, j) if j >i

then X ← MATRIX-CHAIN-MULTIPLY(A, s, i, s[i, j])

Y ← MATRIX-CHAIN-MULTIPLY(A, s, s[i, j] + 1, j) return MATRIX-MULTIPLY(X, Y)

else return A[i] •  It is a recursive algorithm

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Elements of dynamic programming

•  Optimal substructure – The optimal solution for a problem consists of

optimal solutions of subproblems. •  Overlapping subproblems

– Solving a subproblem leads to same subproblems over and over.

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Pattern to discover optimal substructure

•  Solution to the problem depends on one or more subproblems to be solved.

•  Assume that the optimal solution has been given. •  Determine ensued subproblems and characterize its

resulting space best. •  Show the solutions to the subproblems used within

the optimal solution must themselves be optimal by using a "cut-and-paste" technique.

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Overlapping subproblems

•  The recursion tree for the computation of RECURSIVE-MATRIX-CHAIN(p)

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Memoized recursive algorithm •  Bottom-up v.s. top-down •  Memoize the intermediate result encountered first. •  Memoized method

MEMOIZED-MATRIX-CHAIN(p) n ← length[p] - 1

for i ← 1 to n

do for j ← i to n

do m[i, j] ← ∞ return LOOKUP-CHAIN(p, 1, n)

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Memoized recursive algorithm(Cont.)

LOOKUP-CHAIN(p, i, j) //lookup while computing if m[i,j] < ∞ then return m[i, j] if i = j then m[i, j] ← 0 else for k ← i to j - 1 do q ← LOOKUP-CHAIN(p, i, k)

+ LOOKUP-CHAIN(p, k+1, j) + pi - 1pkpj if q < m[i, j] then m[i, j] ← q return m[i, j]

37

Memoized recursive algorithm(Cont.)

•  Running time T(n) = O(n3)

•  Memoization v.s. bottom-up – Bottom-up wins: subproblem must be solved at

least once. – Top-down wins: if some subproblems need not

be solved.

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Dynamic v.s. Divide-and-conquer

•  Divide-and-conquer algorithms partition the problem into independent subproblems, solve the subproblems recursively, and the combine the solutions to solve the original problem.

•  Dynamic programming is applicable when the subproblems are not independent. Every subproblem is solved only once and the result sorted in a table for avoiding the work of recomputing it.

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Dynamic v.s. Divide-and-conquer

•  A consequence is that there must be only relatively few subproblems for the table to be efficiently computable. Under such circumstances dynamic programming allows an exponential-time algorithm to be transformed to a polynomial-time algorithm.

•  The name dynamic programming is historic. It refers to computing the table.

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Next week

•  Greedy algorithm