12 greeddy method

Analysis of AlgorithmsGreedy Methods

Andres Mendez-Vazquez

November 19, 2014

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Outline

1 Greedy MethodSteps of the greedy methodDynamic programming vs Greedy Method

2 Greedy Method ExamplesKnapsack ProblemFractional KnapsackActivity selectionHuffman codes

Some lemmas

3 Exercises

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Outline

Some lemmas

3 Exercises

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Steps of the greedy method

Proceed as followsDetermine the optimal substructure.Develop a recursive solution.Prove that at any stage of recursion, one of the optimal choices is thegreedy choice.Show that all but one of the sub-problems resulting from the greedychoice are empty.Develop a recursive greedy algorithm.Convert it to an iterative algorithm.

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Outline

Some lemmas

3 Exercises

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Dynamic Programming vs Greedy Method

Dynamic ProgrammingMake a choice at each stepChoice depends on knowing optimal solutions to sub-problems. Solvesub-problems first.Solve bottom-up.

Greedy MethodMake a choice at each step.Make the choice before solving the sub-problems.Solve top-down.

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Dynamic Programming vs Greedy Method

Dynamic ProgrammingMake a choice at each stepChoice depends on knowing optimal solutions to sub-problems. Solvesub-problems first.Solve bottom-up.

Greedy MethodMake a choice at each step.Make the choice before solving the sub-problems.Solve top-down.

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Outline

Some lemmas

3 Exercises

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You are a thief

You get into a storeWith a knapsack/bag

The bag has capacity WYou want to select items to fill the bag...

QuestionHow do you do it?

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You are a thief

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You are a thief

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Formalization

FirstYou have n items.Each item is worth vi and it weights wi pounds.The knapsack can stand a weight of W .

SecondYou need to find a subset of items with total weight ≤W such thatyou have the best profit!!!After all you want to be a successful THIEF!!!

Decisions?You can actually use a vector to represent your decisions

〈x1, x2, x3, ..., xn〉 (1)

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Formalization

〈x1, x2, x3, ..., xn〉 (1)

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Formalization

〈x1, x2, x3, ..., xn〉 (1)

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You have two versions

0-1 knapsack problemYou have to either take an item or not take it, you cannot take afraction of it.Thus, elements in the vector are xi ∈ {0, 1} with i = 1, ..., n.

Fractional knapsack problemLike the 0-1 knapsack problem, but you can take a fraction of an item.Thus, elements in the vector are xi ∈ [0, 1] with i = 1, ..., n.

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You have two versions

0-1 knapsack problemYou have to either take an item or not take it, you cannot take afraction of it.Thus, elements in the vector are xi ∈ {0, 1} with i = 1, ..., n.

Fractional knapsack problemLike the 0-1 knapsack problem, but you can take a fraction of an item.Thus, elements in the vector are xi ∈ [0, 1] with i = 1, ..., n.

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Greedy Process for 0-1 Knapsack

First, You choose an orderingWhat about per price of each item?

If we have the following situation

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Greedy Process for 0-1 Knapsack

First, You choose an orderingWhat about per price of each item?

If we have the following situation

item 1

item 2

item 3

KNAPSACK

20 kg10 kg

$100$80

ORDER OF SELECTION

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It works fine

item 1

KNAPSACK50 kg

$100+$120=$220$80

What about this?

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ThusIt works fine

item 1

KNAPSACK50 kg

$100+$120=$220$80

What about this?

item 1 item 2 item 3

KNAPSACK

$100$80

ORDER OF SELECTION

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Thus, we need a better way to select elements!!!

ActuallyWhy not to use the price of kg?

Thus, we have this!!!

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Thus, we need a better way to select elements!!!

ActuallyWhy not to use the price of kg?

Thus, we have this!!!

KNAPSACK

$100$80

ORDER OF SELECTION?

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Did you notice this?

item 1

KNAPSACK

$100+$120=220

Second

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Did you notice this?First

item 1

KNAPSACK

$100+$120=220

SecondKNAPSACK

$40+$80+$100=220

20 kg 20 kg

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However!!!

Even with an order based in vw 0-1 Knapsack fails!!!

KNAPSACK

$75$75

ORDER OF SELECTION?

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Outline

Some lemmas

3 Exercises

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Definition of Fractional Process

FirstPush object indexes into a max heap using the key vi

wifor i = 1, ..., n.

ThenExtract index at the top of the max heap.Take the element represented by the index at the top of the max heapand push it into the knapsack.Reduce the remaining carry weight by the weight of the element

FinallyIf a fraction of space exist, push the next element fraction sorted by keyinto the knapsack.

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wifor i = 1, ..., n.

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wifor i = 1, ..., n.

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Theorem about Greedy Choice

TheoremThe greedy choice, which always selects the object with better ratiovalue/weight, always finds an optimal solution to the Fractional Knapsackproblem.

ProofConstraints:

xi ∈ [0, 1]

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Theorem about Greedy Choice

TheoremThe greedy choice, which always selects the object with better ratiovalue/weight, always finds an optimal solution to the Fractional Knapsackproblem.

ProofConstraints:

xi ∈ [0, 1]

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Fractional GreedyFRACTIONAL-KNAPSACK(W , w , v)

1 for i = 1 to n do x [i ] = 02 weight = 03 // Use a Max-Heap4 T = Build-Max-Heap(v/w)

5 while weight < W do6 i = T.Heap-Extract-Max()7 if (weight + w [i ] ≤W ) do8 x [i ] = 19 weight = weight + w [i ]10 else11 x [i ] = W−weight

w [i]12 weight = W13 return x

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Fractional Greedy

ComplexityUnder the fact that this algorithm is using a heap we can get thecomplexity O(n log n).If we assume already an initial sorting or use a linear sorting we getcomplexity O(n).

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Outline

Some lemmas

3 Exercises

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Activity selection

ProblemSet of activities S = a1, ..., an. The ai activity needs a resource (ClassRoom, Machine, Assembly Line, etc) during period [si , fi), which is ahalf-open interval, where si is the start time of activity ai and fi is thefinish time of activity ai .

For examplei 1 2 3 4 5 6 7 8 9si 1 2 4 1 5 8 9 11 13fi 3 5 7 8 9 10 11 14 16

Goal:Select the largest possible set of non-overlapping activities.

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Activity selection

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Activity selection

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We have something like this

Example

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

ACTIVITY

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We have something like this

Example

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

ACTIVITY

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For Example

Example

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

ACTIVITY

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Thus!!!

FirstThe Optimal Substructure!!!

SecondWe need the recursive solution!!!

ThirdThe Greedy Choice

FourthProve it is the only one!!!

FifthWe need the iterative solution!!!

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Thus!!!

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Thus!!!

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Thus!!!

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Thus!!!

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How do we discover the Optimal Structure

We know we have the followingS = a1, ..., an, a set of activities.

We can then refine the set of activities in the following wayWe define:

Sij = the set of activities that start after activity ai finishes and thatfinishes before activity aj start.

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How do we discover the Optimal Structure

We know we have the followingS = a1, ..., an, a set of activities.

We can then refine the set of activities in the following wayWe define:

Sij = the set of activities that start after activity ai finishes and thatfinishes before activity aj start.

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The Optimal Substructure

Thus, we have

Activity time from less to more

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SecondSuppose that the set Aij denotes the maximum set of compatibleactivities for Sij

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For Example

Thus, we have

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ThirdIn addition assume that Aij includes some activity ak .Then, imagine that ak belongs to some optimal solution.

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What do we need?

We need to find the optimal solutions for Sik and Skj .

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ThenWe need to prove the optimal substructure:

For this we will useI The Cut-and-Paste ArgumentI Contradiction

FirstWe have that Aik = Sik ∩ Aij and similarly Akj = Skj ∩ Aij .

ThenAij = Aik ∪ {ak} ∪ Akj or |Aij | = |Aik |+ |Akj |+ 1

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For Example

We need to find the optimal solutions for Sik and Skj .

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The Optimal Substructure: Using Contradictions

Use Cut-and-Paste ArgumentHow? Assume that exist A′

ik such that∣∣∣A′

∣∣∣ > |Aik |.

MeaningWe assume that we cannot construct the large answer using the smallanswers!!!The Basis of Contradiction (S ∪ {¬P} ` F ) =⇒ (S ` P)

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The Optimal Substructure: Using Contradictions

Use Cut-and-Paste ArgumentHow? Assume that exist A′

∣∣∣ > |Aik |.

MeaningWe assume that we cannot construct the large answer using the smallanswers!!!The Basis of Contradiction (S ∪ {¬P} ` F ) =⇒ (S ` P)

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Important

Here¬P =exist A′

∣∣∣ > |Aik |.

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This means that A′ij has more activities than Aij

Then∣∣∣A′

∣∣∣ = ∣∣∣A′ik

∣∣∣+ |Akj |+ 1 > |Aik |+ |Akj |+ 1 = |Aij | , which is acontradiction.

FinallyWe have the optimal-substructure.

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This means that A′ij has more activities than Aij

Then∣∣∣A′

∣∣∣ = ∣∣∣A′ik

∣∣∣+ |Akj |+ 1 > |Aik |+ |Akj |+ 1 = |Aij | , which is acontradiction.

FinallyWe have the optimal-substructure.

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GivenAij = Aik ∪ {ak} ∪ Akj or |Aij | = |Aik |+ |Akj |+ 1.

QuestionCan anybody give me the recursion?

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GivenAij = Aik ∪ {ak} ∪ Akj or |Aij | = |Aik |+ |Akj |+ 1.

QuestionCan anybody give me the recursion?

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Recursive Formulation

c[i , j ] =

0 if Sij = ∅maxi<k<jak ∈Sij

c[i , k] + c[k , j ] + 1 if Sij 6= ∅

Did you notice that you can use Dynamic Programming?

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Recursive Formulation

c[i , j ] =

0 if Sij = ∅maxi<k<jak ∈Sij

c[i , k] + c[k , j ] + 1 if Sij 6= ∅

Did you notice that you can use Dynamic Programming?

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We have our Goal?

ThusAny Ideas to obtain the largest possible set using Greedy Choice?

What about?Early Starting Time?Early Starting + Less Activity Length?

Me!!!Do we have something that combines both things?

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We have our Goal?

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We have our Goal?

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Which Greedy Choice?

Did you notice the following

Active

Finishing Time

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The Early Finishing Time

Yes!!!The Greedy Choice = Select by earliest finishing time.

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Greedy solution

Theorem 16.1Consider any nonempty subproblem Sk = {ai ∈ S|si > fk}, and let am bean activity in Sk with the earliest finish time. Then am is included in somemaximum-size subset of mutually compatible activities of Sk .

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We can do the followingWe can repeatedly choose the activity that finishes first.Remove all activities incompatible with this activityThen repeat

Not only thatBecause we always choose the activity with the earliest finish time.

I Then, finish times of the activities we choose must strictly increase.

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We can do the followingWe can repeatedly choose the activity that finishes first.Remove all activities incompatible with this activityThen repeat

Not only thatBecause we always choose the activity with the earliest finish time.

I Then, finish times of the activities we choose must strictly increase.

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Top-Down Approach

Something NotableAn algorithm to solve the activity-selection problem does not need to workbottom-up!!!

InsteadIt can work top-down, choosing an activity to put into the optimal solutionand then solving the subproblem of choosing activities from those that arecompatible with those already chosen.

ImportantGreedy algorithms typically have this top-down design:

They make a choice and then solve one subproblem.

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Top-Down Approach

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Top-Down Approach

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FirstThe activities are already sorted by finishing time

In additionThere are dummy activities:

a0= fictitious activity, f0 = 0.

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FirstThe activities are already sorted by finishing time

In additionThere are dummy activities:

a0= fictitious activity, f0 = 0.

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Recursive Activity Selector

REC-ACTIVITY-SELECTOR(s, f , k , n) - Entry Point (s, f , 0, n)

1 m = k + 12 // find the first activity in Sk to finish3 while m ≤ n and s [m] < f [k]4 m = m + 15 if m ≤ n6 return {am}∪REC-ACTIVITY-SELECTOR(s, f , m, n)7 else return ∅

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Do we need into the recursion?

Did you notice?We remove all the activities before fk!!!

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We can do more...GREEDY-ACTIVITY-SELECTOR(s, f , n)

1 n = s.length2 A={a1}3 k = 14 for m = 2 to n5 if s [m] ≥ f [k]6 A=A∪{am}7 k = m8 return A

Complexity of the algorithmΘ(n)

Note: Clearly, we are not taking into account the sorting of theactivities.

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We can do more...GREEDY-ACTIVITY-SELECTOR(s, f , n)

1 n = s.length2 A={a1}3 k = 14 for m = 2 to n5 if s [m] ≥ f [k]6 A=A∪{am}7 k = m8 return A

Complexity of the algorithmΘ(n)

Note: Clearly, we are not taking into account the sorting of theactivities.

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Outline

Some lemmas

3 Exercises

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Huffman codes

UseHuffman codes are widely used and very effective technique forcompressing.

AdvantagesSavings of 20% to 90% are typical, depending on the characteristics of thedata being compressed.

IdeaHuffman codes are based on the idea of prefix codes:

Codes in which no codeword is also a prefix of some other codeword.It is possible to show that the optimal data compression achievable bya character code can always be achieved with a prefix code.

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Huffman codes

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Huffman codes

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Imagine the following

We have the followingImagine having 100,000-character data file, and we want to store itcompactly.In addition, we have the following distribution of character in thehundred of thousands.

Tablea b c d e f

Frequency 45,000 13,000 12,000 16,000 9,000 5,000Fixed-Leng cw 000 001 010 011 100 101Vari Leng cw 0 101 100 111 1101 1100

Table : Distribution of characters in the text and their codewords.

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Imagine the following

We have the followingImagine having 100,000-character data file, and we want to store itcompactly.In addition, we have the following distribution of character in thehundred of thousands.

Tablea b c d e f

Frequency 45,000 13,000 12,000 16,000 9,000 5,000Fixed-Leng cw 000 001 010 011 100 101Vari Leng cw 0 101 100 111 1101 1100

Table : Distribution of characters in the text and their codewords.

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We have the following representations for the previouscodes

Fix Code: No optimal tree in our problem

0 0 01 1 1

a:45 b:13 c:12 d:16 e:9 f:5

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We have the following representations for the previouscodes

Prefix Code: Binary tree for the variable prefix code in table

b:13c:12 d:16

e:9 f:5

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Cost in the number of bits to represent the text

Fix Code

3× 100, 000 = 300, 000 bits (2)

Variable Code

[45× 1+ 13× 3+ 12× 3+ 16× 3+ ...

9× 4+ 5× 4]× 1000 = 224, 000 bits

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Cost in the number of bits to represent the text

Fix Code

3× 100, 000 = 300, 000 bits (2)

Variable Code

[45× 1+ 13× 3+ 12× 3+ 16× 3+ ...

9× 4+ 5× 4]× 1000 = 224, 000 bits

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Prefix Codes

Something NotableIt has been show that prefix codes:

Codes in which no codeword is also a prefix of some other codeword.

They have the following nice propertiesEasy to decode.They are unambiguous.

I For example in our example the string 001011101 transform as0 ◦ 0 ◦ 101 ◦ 1101 = aabe.

PropertiesAs we can prove an optimal code for a text is always represented by a fullbinary tree!!!

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Prefix Codes

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Prefix Codes

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It is more...

Given that we will concentrate our attention for the prefix codes tofull binary tree.Given that C is the alphabet for the text file

We have that1 The tree for the optimal prefix code has |C | leaves.2 The number of internal leaves is |C | − 1.3 Each character x at the leaves has a depth dT (x) which is the length

of the codeword.

ThusKnowing the frequency of each character and the tree T representingthe optimal prefix encoding.We can define the number of bits necessary to encode the text file.

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It is more...

of the codeword.

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It is more...

of the codeword.

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Cost of Trees for Coding

Cost in the number of bits for a text

B(T ) =∑c∈C

c .freq × dT (c).

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Now, Which Greedy Choice?

I leave this to youThe optimal substructure

Greedy ChoiceIdeas?

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Greedy ChoiceIdeas?

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Greedy ChoiceIdeas?

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What about this?

Prefix Code: Binary tree for the variable prefix code in table

b:13c:12 d:16

e:9 f:5

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Greedy Process for Huffman Codes

ProcessYou start with an alphabet C with an associated frequency for eachelement in it.Use the frequencies to build a min priority queue.Subtract the two least frequent elements (Greedy Choice)Build a three using as children the two nodes of the subtreesextracted from the min priority queue. The new root holds the sum offrequencies of the two subtrees.Put it back into the Priority Queue.

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AlgorithmHUFFMAN(C)

1 n = |C |2 Q = C3 for i = 1 to n-14 allocate new node z5 z.left = x = Extract-Min(Q)6 z.right = y = Extract-Min(Q)7 z.freq = x.freq+y.freq8 Insert(Q,z)9 return Extract-Min(Q) // return root of the Huffman Tree

ComplexityΘ(n log n)

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AlgorithmHUFFMAN(C)

1 n = |C |2 Q = C3 for i = 1 to n-14 allocate new node z5 z.left = x = Extract-Min(Q)6 z.right = y = Extract-Min(Q)7 z.freq = x.freq+y.freq8 Insert(Q,z)9 return Extract-Min(Q) // return root of the Huffman Tree

ComplexityΘ(n log n)

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Example

The Process!!!

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Lemmas to sustain the claims

Lemma 16.2Let C be an alphabet in which each character c ∈ C has frequency c.freq.Let x and y be two characters in C having the lowest frequencies. Thenthere exists an optimal prefix code for C in which the codewords for x andy have the same length and differ only in the last bit.

Lemma 16.3Let C be a given alphabet with frequency c:freq defined for each characterc ∈ C . Let x and y be two characters in C with minimum frequency. LetC ′ be the alphabet C with the characters x and y removed and a newcharacter z added, so that C ′ = C − {x , y} ∪ {z}. Define f for C ′ as forC , except that z .freq = x .freq + y .freq. Let T ′ be any tree representingan optimal prefix code for the alphabet C ′. Then the tree T , obtainedfrom T ′ by replacing the leaf node for z with an internal node having xand y as children, represents an optimal prefix code for the alphabet C .

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Lemmas to sustain the claims

Lemma 16.2Let C be an alphabet in which each character c ∈ C has frequency c.freq.Let x and y be two characters in C having the lowest frequencies. Thenthere exists an optimal prefix code for C in which the codewords for x andy have the same length and differ only in the last bit.

Lemma 16.3Let C be a given alphabet with frequency c:freq defined for each characterc ∈ C . Let x and y be two characters in C with minimum frequency. LetC ′ be the alphabet C with the characters x and y removed and a newcharacter z added, so that C ′ = C − {x , y} ∪ {z}. Define f for C ′ as forC , except that z .freq = x .freq + y .freq. Let T ′ be any tree representingan optimal prefix code for the alphabet C ′. Then the tree T , obtainedfrom T ′ by replacing the leaf node for z with an internal node having xand y as children, represents an optimal prefix code for the alphabet C .

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Exercises

From Cormen’s book solve the following16.2-116.2-416.2-516.2-716.1-316.3-316.3-516.3-7

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12 greeddy method

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