the greedy approach - cpp.edugsyoung/cs3310/cs3310notes/greedy.pdf · cs 331 d&a of algo....

CS331 Dr. Young

Greedy

1

YoungCS 331 D&A of Algo.

Greedy 1

The Greedy Approach


Greedy 2

General idea:

Given a problem with n inputs, we are required to obtain a subset that maximizes or minimizes a given objective function subject to some constraints.

Feasible solution — any subset that satisfies some constraints

Optimal solution — a feasible solution that maximizes or minimizes the objective function

CS331 Dr. Young

Greedy

2


Greedy 3

procedure Greedy (A, n)

beginsolution Ø;for i 1 to n do

x Select (A); // based on the objective // function

if Feasible (solution, x), then solution Union (solution, x);

end;

Select: A greedy procedure, based on a given objective function, which selects input from A, removes it and assigns its value to x.

Feasible: A boolean function to decide if x can be included into solution vector (without violating any given constraint).


Greedy 4

About Greedy method

The n inputs are ordered by some selection procedure which is based on some optimization measures.

It works in stages, considering one input at a time. At each stage, a decision is made regarding whether or not a particular input is in an optimal solution.

CS331 Dr. Young

Greedy

3


Greedy 5

1. Minimum Spanning Tree ( For Undirected Graph)

The problem:1) Tree

A Tree is connected graph with no cycles.

2) Spanning Tree

A Spanning Tree of G is a tree which contains all verticesin G.Example:

G:


Greedy 6

b) Is G a Spanning Tree?

Key: Yes Key: No

Note: Connected graph with n vertices and exactly n – 1 edges is Spanning Tree.

3) Minimum Spanning Tree

Assign weight to each edge of G, then Minimum Spanning Tree is the Spanning Tree with minimum total weight.

CS331 Dr. Young

Greedy

4


Greedy 7

Example:a) Edges have the same weight

G: 1

2 3

4 5 6 7

8


Greedy 8

DFS (Depth First Search)

1

2 3

4

5

6

7

8

CS331 Dr. Young

Greedy

5


Greedy 9

BFS (Breadth First Search)

1

2 3

4 5 6 7

8


Greedy 10

b) Edges have different weights

G:

DFS

1 2

3

45

6

16

19

21

33

11

14

18

10

5

6

1

5

2

3

4

6

16

5

10

143378

331410516

Cost

CS331 Dr. Young

Greedy

6


Greedy 11

BFS

Minimum Spanning Tree (with the least total weight)

1 2 3

4

5

6

16

1921

5

6

67

65211916

Cost

1 2

3

4 56

165

611

18 56

18116516

Cost


Greedy 12

Algorithms:1) Prim’s Algorithm (Minimum Spanning Tree)

Basic idea:Start from vertex 1 and let T Ø (T will contain all edges in the S.T.); the next edge to be included in T is the minimum cost edge(u, v), s.t. u is in the tree and v is not.

Example: G1 2

3

45

6

16

19

21

33

11

14

18

10

5

6

CS331 Dr. Young

Greedy

7


Greedy 13

1

2 5 6

1619 21

1 2

5 6 3 64

19 21 5 6 11

1 1 2

1 2 3

56 4 6 4

1921 6 11 10

1 2 3 4

5 6 6 5 6

19 21 11 18 14

1 2 3 1 2 3

4

S.T. S.T.

S.T.S.T.

(Spanning Tree)


Greedy 14

1 2 3 4 6

5 5 5

19 18 33

1 2 3

4 6

S.T.

(n – # of vertices, e – # of edges)It takes O(n) steps. Each step takes O(e) and e n(n-1)/2Therefore, it takes O(n3) time.

With clever data structure, it can be implemented in O(n2).

).( 2nO

1 2 3

4 6

5

S.T.

Cost = 16+5+6+11+18=56Minimum Spanning Tree

CS331 Dr. Young

Greedy

8


Greedy 15

Example:

Step 1: Sort all of edges(1,2) 10 √(3,6) 15 √(4,6) 20 √(2,6) 25 √(1,4) 30 × reject create cycle(3,5) 35 √

1 2

3

5

4

6

1050

35

40

55

15

4525

30

20

2) Kruskal’s Algorithm

Basic idea:

Don’t care if T is a tree or not in the intermediate stage, as long as the including of a new edge will not create a cycle, we include the minimum cost edge


Greedy 16

Step 2: T

1 2

1 2 3 6

1 2 3 6 4

1 2 3 6 4

1 2 3 6 4

5

CS331 Dr. Young

Greedy

9


Greedy 17

How to check:

adding an edge will create a cycle or not?

If Maintain a set for each group(initially each node represents a set)Ex: set1 set2 set3

new edge

from different groups no cycle created

Data structure to store sets so that:

i. The group number can be easily found, and

ii. Two sets can be easily merged

1 2 3 6 4 5

2 6


Greedy 18

While (T contains fewer than n-1 edges) and (E ) doBegin

Choose an edge (v,w) from E of lowest cost;Delete (v,w) from E;If (v,w) does not create a cycle in Tthen add (v,w) to T else discard (v,w);

End;

With clever data structure, it can be implemented in O(e log e).

Kruskal’s algorithm

CS331 Dr. Young

Greedy

10


Greedy 19

So, complexity of Kruskal is )(eLogeO

LognLognLogenn

e 22

)1( 2

)()( eLognOeLogeO

3) Comparing Prim’s Algorithm with Kruskal’s Algorithm

i. Prim’s complexity is

ii. Kruskal’s complexity is

if G is a complete (dense) graph, Kruskal’s complexity is .

if G is a sparse graph, Kruskal’s complexity is

)( 2nO

)(eLognO

)( 2LognnO

).(nLognO


Greedy 20

2. Dijkstra’s Algorithm for Single-Source Shortest Paths

The problem: Given directed graph G = (V, E), a weight for each edge in G,a source node v0,

Goal: determine the (length of) shortest paths from v0 to all the remaining vertices in G

Def: Length of the path: Sum of the weight of the edges

Observation:May have more than 1 paths between w and x (y and z)But each individual path must be minimal length

(in order to form an overall shortest path form v0 to vi )

V0 Vi

w x y z

shortest paths from v0 to vi

CS331 Dr. Young

Greedy

11


Greedy 21

1 if shortest path (v0 , w) is defined0 otherwises(w) =

jjDist )( in the vertex set V

= the length of the shortest path from v0 to j

ijFrom )( if i is the predecessor of jalong the shortest path from v0 to j

Notation

cost adjacency matrix Cost, 1 a,b V

Cost (a, b) = cost from vertex i to vertex j if there is a edge0 if a = b otherwise


Greedy 22

Example:

V0 V1

V2 V3

V4

V5

50 10

2010

15

15

20

3

35

30

45

a) Cost adjacent matrix

03

030

35020

15020

10150

4510500

5

4

3

2

1

0

543210

v

v

v

v

v

v

vvvvvv

CS331 Dr. Young

Greedy

12


Greedy 23

b) Steps in Dijkstra’s Algorithm

1. Dist (v0) = 0, From (v0) = v0 2. Dist (v2) = 10, From (v2) = v0

V0 V1

V2 V3

V4

V5

50 10

315

2035

30

1520 10

45

V0

V2

V1

V3

V4

V5

50 10

2010

15

15

2035

30

3

45


Greedy 24

3. Dist (v3) = 25, From (v3) = v2 4. Dist (v1) = 45, From (v1) = v3

V0

V2 V3

V1 V4

V5

50 10

45

20 10

15

15

20

35

3

V0

V2

V1

V3

V4

V5

50

45

10

2010

15

15

20

35

30

3

30

CS331 Dr. Young

Greedy

13


Greedy 25

5. Dist (v4) = 45, From (v4) = v0 6. Dist (5) =

V0 V1

V2 V3

V4

V5

45

50 10

20 10

15

20

15

35

30

3

V0 V1

V2 V3

V4

V5

45

50 10

20 10

15

20

15

35

30

3


Greedy 26

c) Shortest paths from source v0

v0 v2 v3 v1 45

v0 v2 10

v0 v2 v3 25

v0 v4 45

v0 v5

CS331 Dr. Young

Greedy

14


Greedy 27

Dijkstra’s algorithm:

procedure Dijkstra (Cost, n, v, Dist, From)// Cost, n, v are input, Dist, From are output

beginfor i 1 to n do

for num 1 to (n – 1) do choose u s.t. s(u) = 0 and Dist(u) is minimum;

for all w with s(w) = 0 doif

end;

(cont. next page)

;)(

);,()(

;0)(

viFrom

ivCostiDist

is

;1)( vs

;1)( us

;)(

);,()()(

uwFrom

wuCostuDistwDist

))(),()(( wDistwuCostuDist


Greedy 28

Ex: 1

25

34

10 50

10

100 30

20

50

5

a) Cost adjacent matrix

010

020

5005

0

1010030500

5

4

3

2

1

0504030201

CS331 Dr. Young

Greedy

15


Greedy 29

b) Steps in Dijkstra’s algorithm

1. Dist (1) = 0, From (1) = 1 2. Dist (5) = 10, From (5) = 1

1

25

34

10 50

10

100 30

20

50

5

50

1

2

5

34

10 50

10

100 30

205


Greedy 30

3. Dist (4) = 20, From (4) = 5 4. Dist (3) = 30, From (3) = 1

1

25

34

10 50

10

100 30

20

50

5

1

25

34

10 50

10

100 30

20

50

5

5. Dist (2) = 35, From (2) = 31

25

34

10 50

10

100 30

20

50

5

Shortest paths from source 1

1 3 2 35

1 3 30

1 5 4 20

1 5 10

CS331 Dr. Young

Greedy

16


Greedy 31

3. Optimal Storage on Tapes

The problem:

Given n programs to be stored on tape, the lengths of these n programs are l1, l2 , . . . , ln respectively.Suppose the programs are stored in the order of i1, i2 , . . . , in

Let tj be the time to retrieve program ij.

Assume that the tape is initially positioned at the beginning.

tj is proportional to the sum of all lengths of programs stored in front of the program ij.


Greedy 32

The goal is to minimize MRT (Mean Retrieval Time),

i.e. want to minimize

n

jjt

n 1

1

n

j

j

kkil

1 1

Ex:There are n! = 6 possible orderings for storing them.

)3,10,5(),,(,3 321 llln

order total retrieval time MRT

123456

1 2 31 3 22 1 32 3 13 1 23 2 1

5+(5+10)+(5+10+3)=385+(5+3)+(5+3+10)=31

10+(10+5)+(10+5+3)=4310+(10+3)+(10+3+5)=413+(3+5)+(3+5+10)=29

3+(3+10)+(3+10+5)=34

38/331/343/341/329/334/3

Note: The problem can be solved using greedy strategy,just always let the shortest program goes first.

( Can simply get the right order by using any sorting algorithm)

Smallest

CS331 Dr. Young

Greedy

17


Greedy 33

Analysis:

Try all combination: O( n! )

Shortest-length-First Greedy method: O (nlogn)

Shortest-length-First Greedy method: Sort the programs s.t. and call this ordering L.

Next is to show that the ordering L is the best

Proof by contradiction:Suppose Greedy ordering L is not optimal, then there exists some other permutation I that is optimal.

I = (i1, i2, … in) a < b, s.t. (otherwise I = L)ba ii ll

nlll . . .21


Greedy 34

Interchange ia and ib in and call the new list I :I

I

… ia ia+1 ia+2 … ib …

x

… ib ia+1 ia+2 … ia …

xIn I, Program ia+1 will take less (lia

- lib) time than in I to be retrieved

In fact, each program ia+1 , …, ib-1 will take less (lia- lib

) time For ib, the retrieval time decreases x + liaFor ia, the retrieval time increases x + lib

)(0))((

)()())(1()()(

ba

baba

ii

iiii

llab

lxlxllabItotalRTItotalRT

Therefore, greedy ordering L is optimal Contradiction!!

SWAP

CS331 Dr. Young

Greedy

18


Greedy 35

4. Knapsack Problem

The problem:Given a knapsack with a certain capacity M,n objects, are to be put into the knapsack, each has a weight and a profit if put in the knapsack .

The goal is find where

s.t. is maximized and

Note: All objects can break into small piecesor xi can be any fraction between 0 and 1.

nwww ,,, 21

nppp ,,, 21

),,,( 21 nxxx 10 ix

n

iii xp

1

n

iii Mxw

1


Greedy 36

Example:

)15,24,25(),,(

)10,15,18(),,(

20

3

321

321

ppp

www

M

n

Greedy Strategy#1: Profits are ordered in nonincreasing order (1,2,3)

2.2801515

224125

)0,15

2,1(),,(

3

1

321

i

ii xp

xxx

CS331 Dr. Young

Greedy

19


Greedy 37

Greedy Strategy#2: Weights are ordered in nondecreasing order (3,2,1)

311153

224025

)1,3

2,0(),,(

3

1

321

i

ii xp

xxx

Greedy Strategy#3: p/w are ordered in nonincreasing order (2,3,1)

5.312

115124025

)2

1,1,0(),,(

)1,3,2(

5.110

15

6.115

24

4.118

25

3

1

321

3

3

2

2

1

1

i

ii xp

xxx

w

p

w

p

w

p

Optimal solution


Greedy 38

Analysis:Sort the p/w, such that Show that the ordering is the best.

Proof by contradiction:Given some knapsack instanceSuppose the objects are ordered s.t.

let the greedy solution be

Show that this ordering is optimalCase1: it’s optimalCase2: s.t.

where

n

n

w

p

w

p

w

p

2

2

1

1

n

n

w

p

w

p

w

p

2

2

1

1

),,( 21 nxxxX

)1,,1,1( X)0,,0,,,1,1( jxX

n

iii Mxw

1

10 jx

CS331 Dr. Young

Greedy

20


Greedy 39

Assume X is not optimal, and then there exists

s.t. and Y is optimal

examine X and Y, let yk be the 1st one in Y that yk xk.

),,,( 21 nyyyY

n

iii

n

iii xpyp

11

),,,,,,( 121 nkk xxxxxX

),,,,,,( 121 nkk yyyyyY

Now we increase yk to xk and decrease as many of as necessary, so that the capacity is still M.

),,( 1 nk yy

same yk xk yk < xk


Greedy 40

Let this new solution be

where

),,,( 21 nzzzZ kixz ii 1

n

kiiiikkk wzywyzand

1

)()(

n

iii

k

kn

kiiiikkk

n

iii

n

iii

ik

ki

i

i

k

k

n

kiiiikkk

n

iii

n

iii

ypw

pwzywyzypzp

ww

pp

kiw

p

w

p

pzypyzypzp

1111

111

)()(

)(

1

)()(

0

CS331 Dr. Young

Greedy

21


Greedy 41

So,if

else (Repeat the same process. At the end, Y can be transformed into X. X is also optimal.Contradiction! )

)()()( yprofitzprofit

)()( yprofitzprofit

the greedy approach - cpp.edugsyoung/cs3310/cs3310notes/greedy.pdf · cs 331 d&a of algo....

Documents