5.3 dynamic programming

Chapter 8Chapter 8
Dynamic ProgrammingDynamic Programming
Copyright © 2007 Pearson Addison-Wesley. All rights reserved.

8-2Copyright © 2007 Pearson Addison-Wesley. All rights reserved. A. Levitin “Introduction to the Design & Analysis of Algorithms,” 2nd
ed., Ch. 8
Dynamic ProgrammingDynamic Programming
DDynamic Programmingynamic Programming is a general algorithm design techniqueis a general algorithm design technique
for solving problems defined by or formulated as recurrencesfor solving problems defined by or formulated as recurrences
with overlapping subinstanceswith overlapping subinstances
• Invented by American mathematician Richard Bellman in theInvented by American mathematician Richard Bellman in the
1950s to solve optimization problems and later assimilated by CS1950s to solve optimization problems and later assimilated by CS
• ““Programming” here means “planning”Programming” here means “planning”
• Main idea:Main idea:
- set up a recurrence relating a solution to a larger instanceset up a recurrence relating a solution to a larger instance
to solutions of some smaller instancesto solutions of some smaller instances
- solve smaller instances once- solve smaller instances once
- record solutions in a tablerecord solutions in a table
- extract solution to the initial instance from that tableextract solution to the initial instance from that table

ed., Ch. 8
Example: Fibonacci numbersExample: Fibonacci numbers
• Recall definition of Fibonacci numbers:Recall definition of Fibonacci numbers:
FF((nn)) = F= F((nn-1)-1) + F+ F((nn-2)-2)
FF(0)(0) == 00
FF(1)(1) == 11
• Computing theComputing the nnthth
Fibonacci number recursively (top-down):Fibonacci number recursively (top-down):
FF((nn))
FF((n-n-1)1) + F+ F((n-n-2)2)
FF((n-n-2)2) + F+ F((n-n-3)3) FF((n-n-3)3) + F+ F((n-n-4)4)
......

ed., Ch. 8
Example: Fibonacci numbers (cont.)Example: Fibonacci numbers (cont.)
Computing theComputing the nnthth
Fibonacci number using bottom-up iteration andFibonacci number using bottom-up iteration and
recording results:recording results:
FF(0)(0) == 00
FF(1)(1) == 11
FF(2)(2) == 1+0 = 11+0 = 1
……
FF((nn-2) =-2) =
FF((nn-1) =-1) =
FF((nn) =) = FF((nn-1)-1) + F+ F((nn-2)-2)
Efficiency:Efficiency:
- time- time
- space- space
0 1 1 . . . F(n-2) F(n-1) F(n)
n
n
What if we solve
it recursively?

ed., Ch. 8
Examples of DP algorithmsExamples of DP algorithms
• Computing a binomial coefficientComputing a binomial coefficient
• Longest common subsequenceLongest common subsequence
• Warshall’s algorithm for transitive closureWarshall’s algorithm for transitive closure
• Floyd’s algorithm for all-pairs shortest pathsFloyd’s algorithm for all-pairs shortest paths
• Constructing an optimal binary search treeConstructing an optimal binary search tree
• Some instances of difficult discrete optimization problems:Some instances of difficult discrete optimization problems:
- traveling salesman- traveling salesman
- knapsack- knapsack

ed., Ch. 8
Computing a binomial coefficient by DPComputing a binomial coefficient by DP
Binomial coefficients are coefficients of the binomial formula:Binomial coefficients are coefficients of the binomial formula:
((a + ba + b))nn
== CC((nn,0),0)aann
bb00
+ . . . ++ . . . + CC((nn,,kk))aan-kn-k
bbkk
+ . . . +. . . + CC((nn,,nn))aa00
bbnn
Recurrence:Recurrence: CC((nn,,kk) =) = CC((n-n-1,1,kk) +) + CC((nn-1,-1,kk-1) for-1) for n > kn > k > 0> 0
CC((nn,0) = 1,,0) = 1, CC((nn,,nn) = 1 for) = 1 for nn ≥≥ 00
Value ofValue of CC((nn,,kk) can be computed by filling a table:) can be computed by filling a table:
0 1 2 . . .0 1 2 . . . kk-1-1 kk
0 10 1
1 1 11 1 1
..
..
..
n-n-11 CC((n-n-1,1,kk-1)-1) CC((n-n-1,1,kk))
nn CC((nn,,kk))

ed., Ch. 8
ComputingComputing CC((n,kn,k): pseudocode and analysis): pseudocode and analysis
Time efficiency:Time efficiency: ΘΘ((nknk))
Space efficiency:Space efficiency: ΘΘ((nknk))

ed., Ch. 8
Knapsack Problem by DPKnapsack Problem by DP
GivenGiven nn items ofitems of
integer weights:integer weights: ww11 ww22 … w… wnn
values:values: vv11 vv22 … v… vnn
a knapsack of integer capacitya knapsack of integer capacity WW
find most valuable subset of the items that fit into the knapsackfind most valuable subset of the items that fit into the knapsack
Consider instance defined by firstConsider instance defined by first ii items and capacityitems and capacity jj ((jj ≤≤ WW))..
LetLet VV[[ii,,jj] be optimal value of such an instance. Then] be optimal value of such an instance. Then
max {max {VV[[ii-1,-1,jj],], vvii ++ VV[[ii-1,-1,j-j- wwii]} if]} if j-j- wwii ≥≥ 00
VV[[ii,,jj] =] =
VV[[ii-1,-1,jj] if] if j-j- wwii < 0< 0
Initial conditions:Initial conditions: VV[0,[0,jj] = 0 and] = 0 and VV[[ii,0] = 0,0] = 0
{

ed., Ch. 8
Knapsack Problem by DP (example)Knapsack Problem by DP (example)
Example: Knapsack of capacityExample: Knapsack of capacity WW = 5= 5
item weight valueitem weight value
1 2 $121 2 $12
2 1 $102 1 $10
3 3 $203 3 $20
4 2 $15 capacity4 2 $15 capacity jj
0 1 2 3 40 1 2 3 4 55
00
ww11 = 2,= 2, vv11==12 112 1
ww22 = 1,= 1, vv22==10 210 2
ww33 = 3,= 3, vv33==20 320 3
ww44 = 2,= 2, vv44==15 415 4 ??
0 0 0
0 0 12
0 10 12 22 22 22
0 10 12 22 30 32
0 10 15 25 30 37
Backtracing
finds the actual
optimal subset,
i.e. solution.

ed., Ch. 8
Knapsack Problem by DP (pseudocode)Knapsack Problem by DP (pseudocode)
Algorithm DPKnapsack(Algorithm DPKnapsack(ww[[11....nn],], vv[[1..n1..n],], WW))
varvar VV[[0..n,0..W0..n,0..W]], P, P[[1..n,1..W1..n,1..W]]:: intint
forfor j := 0j := 0 toto WW dodo
VV[[0,j0,j] :=] := 00
forfor i := 0i := 0 toto nn dodo
VV[[i,0i,0]] := 0:= 0
forfor i := 1i := 1 toto nn dodo
forfor j := 1j := 1 toto WW dodo
ifif ww[[ii]] ≤≤ jj andand vv[[ii]] + V+ V[[i-1,j-wi-1,j-w[[ii]]]] > V> V[[i-1,ji-1,j] then] then
VV[[i,ji,j]] := v:= v[[ii]] + V+ V[[i-1,j-wi-1,j-w[[ii]]]]; P; P[[i,ji,j]] := j-w:= j-w[[ii]]
elseelse
VV[[i,ji,j]] := V:= V[[i-1,ji-1,j]]; P; P[[i,ji,j]] := j:= j
returnreturn VV[[n,Wn,W] and the optimal subset by backtracing] and the optimal subset by backtracing
Running time and space:
O(nW).

ed., Ch. 8
Longest Common Subsequence (LCS)Longest Common Subsequence (LCS)
A subsequence of a sequence/stringA subsequence of a sequence/string SS is obtained byis obtained by
deleting zero or more symbols fromdeleting zero or more symbols from SS. For example, the. For example, the
following arefollowing are somesome subsequences of “president”: pred, sdn,subsequences of “president”: pred, sdn,
predent. In other words, the letters of a subsequence of Spredent. In other words, the letters of a subsequence of S
appear in order inappear in order in SS, but they are not required to be, but they are not required to be
consecutive.consecutive.
The longest common subsequence problem is to find aThe longest common subsequence problem is to find a
maximum length common subsequence between twomaximum length common subsequence between two
sequences.sequences.

ed., Ch. 8
LCSLCS
For instance,For instance,
Sequence 1: presidentSequence 1: president
Sequence 2: providenceSequence 2: providence
Its LCS is priden.Its LCS is priden.
president
providence

ed., Ch. 8
LCSLCS
Another example:Another example:
Sequence 1: algorithmSequence 1: algorithm
Sequence 2: alignmentSequence 2: alignment
One of its LCS is algm.One of its LCS is algm.
a l g o r i t h m
a l i g n m e n t

ed., Ch. 8
How to compute LCS?How to compute LCS?
Let ALet A=a=a11aa22…a…amm andand B=bB=b11bb22…b…bnn ..
lenlen((i, ji, j): the length of an LCS between): the length of an LCS between
aa11aa22…a…aii andand bb11bb22…b…bjj
With proper initializations,With proper initializations, lenlen((i, ji, j) can be computed as follows.) can be computed as follows.
,
.and0,if)),1(),1,(max(
and0,if1)1,1(
,0or0if0
),(





≠>−−
=>+−−
==
=
ji
ji
bajijilenjilen
bajijilen
ji
jilen

ed., Ch. 8
procedure LCS-Length(A, B)
1. for i ← 0 to m dolen(i,0) = 0
2. for j ← 1 to n dolen(0,j) = 0
3. for i ← 1 to m do
4. for j ← 1 to n do
5. if ji ba = then 


=
+−−=
""),(
1)1,1(),(
jiprev
jilenjilen
6. else if )1,(),1( −≥− jilenjilen
7. then 


=
−=
""),(
),1(),(
jiprev
jilenjilen
8. else 


=
−=
""),(
)1,(),(
jiprev
jilenjilen
9. return len and prev

ed., Ch. 8
i j 0 1
p
2
r
3
o
4
v
5
i
6
d
7
e
8
n
9
c
10
e
0 0 0 0 0 0 0 0 0 0 0 0
1 p
2
0 1 1 1 1 1 1 1 1 1 1
2 r 0 1 2 2 2 2 2 2 2 2 2
3 e 0 1 2 2 2 2 2 3 3 3 3
4 s 0 1 2 2 2 2 2 3 3 3 3
5 i 0 1 2 2 2 3 3 3 3 3 3
6 d 0 1 2 2 2 3 4 4 4 4 4
7 e 0 1 2 2 2 3 4 5 5 5 5
8 n 0 1 2 2 2 3 4 5 6 6 6
9 t 0 1 2 2 2 3 4 5 6 6 6
Running time and memory: O(mn) and O(mn).

ed., Ch. 8
procedure Output-LCS(A, prev, i, j)
1 if i = 0 or j = 0 then return
2 if prev(i, j)=” “ then 

 −−−
ia
jiprevALCSOutput
print
)1,1,,(
3 else if prev(i, j)=” “ then Output-LCS(A, prev, i-1, j)
4 else Output-LCS(A, prev, i, j-1)
The backtracing algorithm

ed., Ch. 8
i j 0 1
p
2
r
3
o
4
v
5
i
6
d
7
e
8
n
9
c
10
e
0 0 0 0 0 0 0 0 0 0 0 0
1 p
2
0 1 1 1 1 1 1 1 1 1 1
2 r 0 1 2 2 2 2 2 2 2 2 2
3 e 0 1 2 2 2 2 2 3 3 3 3
4 s 0 1 2 2 2 2 2 3 3 3 3
5 i 0 1 2 2 2 3 3 3 3 3 3
6 d 0 1 2 2 2 3 4 4 4 4 4
7 e 0 1 2 2 2 3 4 5 5 5 5
8 n 0 1 2 2 2 3 4 5 6 6 6
9 t 0 1 2 2 2 3 4 5 6 6 6
Output: priden

ed., Ch. 8
Warshall’s Algorithm: Transitive ClosureWarshall’s Algorithm: Transitive Closure
• Computes the transitive closure of a relationComputes the transitive closure of a relation
• Alternatively: existence of all nontrivial paths in a digraphAlternatively: existence of all nontrivial paths in a digraph
• Example of transitive closure:Example of transitive closure:
3
4
2
1
0 0 1 0
1 0 0 1
0 0 0 0
0 1 0 0
0 0 1 0
1 1 11 1 1
0 0 0 0
11 1 1 11 1
3
4
2
1

ed., Ch. 8
Warshall’s AlgorithmWarshall’s Algorithm
Constructs transitive closureConstructs transitive closure TT as the last matrix in the sequenceas the last matrix in the sequence
ofof nn-by--by-nn matricesmatrices RR(0)(0)
, … ,, … , RR((kk))
, … ,, … , RR((nn))
wherewhere
RR((kk))
[[ii,,jj] = 1 iff there is nontrivial path from] = 1 iff there is nontrivial path from ii toto jj with only thewith only the
firstfirst kk vertices allowed as intermediatevertices allowed as intermediate
Note thatNote that RR(0)(0)
== AA (adjacency matrix)(adjacency matrix),, RR((nn))
= T= T (transitive closure)(transitive closure)
3
42
1
3
42
1
3
42
1
3
42
1
R(0)
0 0 1 0
1 0 0 1
0 0 0 0
0 1 0 0
R(1)
0 0 1 0
1 0 11 1
0 0 0 0
0 1 0 0
R(2)
0 0 1 0
1 0 1 1
0 0 0 0
11 1 1 11 1
R(3)
0 0 1 0
1 0 1 1
0 0 0 0
1 1 1 1
R(4)
0 0 1 0
1 11 1 1
0 0 0 0
1 1 1 1
3
42
1

ed., Ch. 8
Warshall’s Algorithm (recurrence)Warshall’s Algorithm (recurrence)
On theOn the k-k-th iteration, the algorithm determines for every pair ofth iteration, the algorithm determines for every pair of
verticesvertices i, ji, j if a path exists fromif a path exists from ii andand jj with just vertices 1,…,with just vertices 1,…,kk
allowedallowed asas intermediateintermediate
RR((kk-1)-1)
[[i,ji,j]] (path using just 1 ,…,(path using just 1 ,…,k-k-1)1)
RR((kk))
[[i,ji,j] =] = oror
RR((kk-1)-1)
[[i,ki,k] and] and RR((kk-1)-1)
[[k,jk,j]] (path from(path from ii toto kk
and fromand from kk toto jj
using just 1 ,…,using just 1 ,…,k-k-1)1)
i
j
k
{
Initial condition?

ed., Ch. 8
Warshall’s Algorithm (matrix generation)Warshall’s Algorithm (matrix generation)
Recurrence relating elementsRecurrence relating elements RR((kk))
to elements ofto elements of RR((kk-1)-1)
is:is:
RR((kk))
[[i,ji,j] =] = RR((kk-1)-1)
[[i,ji,j] or] or ((RR((kk-1)-1)
[[i,ki,k] and] and RR((kk-1)-1)
[[k,jk,j])])
It implies the following rules for generatingIt implies the following rules for generating RR((kk))
fromfrom RR((kk-1)-1)
::
Rule 1Rule 1 If an element in rowIf an element in row ii and columnand column jj is 1 inis 1 in RR((k-k-1)1)
,,
it remains 1 init remains 1 in RR((kk))
Rule 2Rule 2 If an element in rowIf an element in row ii and columnand column jj is 0 inis 0 in RR((k-k-1)1)
,,
it has to be changed to 1 init has to be changed to 1 in RR((kk))
if and only ifif and only if
the element in its rowthe element in its row ii and columnand column kk and the elementand the element
in its columnin its column jj and rowand row kk are both 1’s inare both 1’s in RR((k-k-1)1)

ed., Ch. 8
Warshall’s Algorithm (example)Warshall’s Algorithm (example)
3
42
1 0 0 1 0
1 0 0 1
0 0 0 0
0 1 0 0
R(0) =
0 0 1 0
1 0 1 1
0 0 0 0
0 1 0 0
R(1) =
0 0 1 0
1 0 1 1
0 0 0 0
1 1 1 1
R(2) =
0 0 1 0
1 0 1 1
0 0 0 0
1 1 1 1
R(3) =
0 0 1 0
1 1 1 1
0 0 0 0
1 1 1 1
R(4) =

ed., Ch. 8
Warshall’s Algorithm (pseudocode and analysis)Warshall’s Algorithm (pseudocode and analysis)
Time efficiency:Time efficiency: ΘΘ((nn33
))
Space efficiency: Matrices can be written over their predecessorsSpace efficiency: Matrices can be written over their predecessors
(with some care), so it’s(with some care), so it’s ΘΘ((nn^2).^2).

ed., Ch. 8
Floyd’s Algorithm: All pairs shortest pathsFloyd’s Algorithm: All pairs shortest paths
Problem: In a weighted (di)graph, find shortest paths betweenProblem: In a weighted (di)graph, find shortest paths between
every pair of verticesevery pair of vertices
Same idea: construct solution through series of matricesSame idea: construct solution through series of matrices DD(0)(0)
, …,, …,
DD ((nn))
using increasing subsets of the vertices allowedusing increasing subsets of the vertices allowed
as intermediateas intermediate
Example:Example: 3
4
2
1
4
1
6
1
5
3
0 ∞ 4 ∞
1 0 4 3
∞ ∞ 0 ∞
6 5 1 0

ed., Ch. 8
Floyd’s Algorithm (matrix generation)Floyd’s Algorithm (matrix generation)
On theOn the k-k-th iteration, the algorithm determines shortest pathsth iteration, the algorithm determines shortest paths
between every pair of verticesbetween every pair of vertices i, ji, j that use only vertices among 1,that use only vertices among 1,
…,…,kk as intermediateas intermediate
DD((kk))
[[i,ji,j] = min {] = min {DD((kk-1)-1)
[[i,ji,j],], DD((kk-1)-1)
[[i,ki,k] +] + DD((kk-1)-1)
[[k,jk,j]}]}
i
j
k
DD((kk-1)-1)
[[i,ji,j]]
DD((kk-1)-1)
[[i,ki,k]]
DD((kk-1)-1)
[[k,jk,j]]
Initial condition?

ed., Ch. 8
Floyd’s Algorithm (example)Floyd’s Algorithm (example)
0 ∞ 3 ∞
2 0 ∞ ∞
∞ 7 0 1
6 ∞ ∞ 0
D(0) =
0 ∞ 3 ∞
2 0 5 ∞
∞ 7 0 1
6 ∞ 9 0
D(1) =
0 ∞ 3 ∞
2 0 5 ∞
9 7 0 1
6 ∞ 9 0
D(2) =
0 10 3 4
2 0 5 6
9 7 0 1
6 16 9 0
D(3) =
0 10 3 4
2 0 5 6
7 7 0 1
6 16 9 0
D(4) =
3
1
3
2
6 7
4
1 2

ed., Ch. 8
Floyd’s Algorithm (pseudocode and analysis)Floyd’s Algorithm (pseudocode and analysis)
))
Space efficiency: Matrices can be written over their predecessorsSpace efficiency: Matrices can be written over their predecessors
Note: Works on graphs with negative edges but without negative cycles.Note: Works on graphs with negative edges but without negative cycles.
Shortest paths themselves can be found, too.Shortest paths themselves can be found, too. How?How?
If D[i,k] + D[k,j] < D[i,j] then P[i,j]  k
Since the superscripts k or k-1 make
no difference to D[i,k] and D[k,j].

ed., Ch. 8
Optimal Binary Search TreesOptimal Binary Search Trees
Problem: GivenProblem: Given nn keyskeys aa11 < …<< …< aann and probabilitiesand probabilities pp11,, …,…, ppnn
searching for them, find a BST with a minimumsearching for them, find a BST with a minimum
average number of comparisons in successful search.average number of comparisons in successful search.
Since total number of BSTs withSince total number of BSTs with nn nodes is given by C(2nodes is given by C(2nn,,nn)/)/
((nn+1), which grows exponentially, brute force is hopeless.+1), which grows exponentially, brute force is hopeless.
Example: What is an optimal BST for keysExample: What is an optimal BST for keys AA,, BB,, CC, and, and DD withwith
search probabilities 0.1, 0.2, 0.4, and 0.3, respectively?search probabilities 0.1, 0.2, 0.4, and 0.3, respectively?
D
A
B
C
Average # of comparisons
= 1*0.4 + 2*(0.2+0.3) + 3*0.1
= 1.7

ed., Ch. 8
DP for Optimal BST ProblemDP for Optimal BST Problem
LetLet CC[[i,ji,j] be minimum average number of comparisons made in] be minimum average number of comparisons made in
T[T[i,ji,j], optimal BST for keys], optimal BST for keys aaii < …<< …< aajj ,, where 1 ≤where 1 ≤ ii ≤≤ jj ≤≤ n.n.
Consider optimal BST among all BSTs with someConsider optimal BST among all BSTs with some aakk ((ii ≤≤ kk ≤≤ jj ))
as their root; T[as their root; T[i,ji,j] is the best among them.] is the best among them.
a
Optimal
BST for
a , ..., a
Optimal
BST for
a , ..., ai
k
k-1 k+1 j
CC[[i,ji,j] =] =
min {min {ppkk ·· 1 +1 +
∑∑ ppss (level(level aass in T[in T[i,k-i,k-1] +1)1] +1) ++
∑∑ ppss (level(level aass in T[in T[k+k+11,j,j] +1)}] +1)}
ii ≤≤ kk ≤≤ jj
ss == ii
k-k-11
s =s =k+k+11
jj

ed., Ch. 8
goal0
0
C[i,j]
0
1
n+1
0 1 n
p 1
p2
np
i
j
DP for Optimal BST Problem (cont.)DP for Optimal BST Problem (cont.)
After simplifications, we obtain the recurrence forAfter simplifications, we obtain the recurrence for CC[[i,ji,j]:]:
CC[[i,ji,j] =] = min {min {CC[[ii,,kk-1] +-1] + CC[[kk+1,+1,jj]} + ∑]} + ∑ ppss forfor 11 ≤≤ ii ≤≤ jj ≤≤ nn
CC[[i,ii,i] =] = ppii for 1for 1 ≤≤ ii ≤≤ jj ≤≤ nn
ss == ii
jj
ii ≤≤ kk ≤≤ jj

Example: keyExample: key A B C DA B C D
probability 0.1 0.2 0.4 0.3probability 0.1 0.2 0.4 0.3
The tables below are filled diagonal by diagonal: the left one is filledThe tables below are filled diagonal by diagonal: the left one is filled
using the recurrenceusing the recurrence
CC[[i,ji,j] =] = min {min {CC[[ii,,kk-1] +-1] + CC[[kk+1,+1,jj]} + ∑]} + ∑ pps ,s , CC[[i,ii,i] =] = ppii ;;
the right one, for trees’ roots, recordsthe right one, for trees’ roots, records kk’s values giving the minima’s values giving the minima
00 11 22 33 44
11 00 .1.1 .4.4 1.11.1 1.71.7
22 00 .2.2 .8.8 1.41.4
33 00 .4.4 1.01.0
44 00 .3.3
55 00
00 11 22 33 44
11 11 22 33 33
22 22 33 33
33 33 33
44 44
55
ii ≤≤ kk ≤≤ jj ss == ii
jj
optimal BSToptimal BST
B
A
C
D
ii
jj
ii
jj

ed., Ch. 8
Optimal Binary Search TreesOptimal Binary Search Trees

ed., Ch. 8
Analysis DP for Optimal BST ProblemAnalysis DP for Optimal BST Problem
) but can be reduced to) but can be reduced to ΘΘ((nn22
)) by takingby taking
advantage of monotonicity of entries in theadvantage of monotonicity of entries in the
root table, i.e.,root table, i.e., RR[[i,ji,j] is always in the range] is always in the range
betweenbetween RR[[i,ji,j-1] and R[-1] and R[ii+1,j]+1,j]
Space efficiency:Space efficiency: ΘΘ((nn22
))
Method can be expanded to include unsuccessful searchesMethod can be expanded to include unsuccessful searches

5.3 dynamic programming

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5.3 dynamic programming