Complexity Basics - West Virginia...

Outline

Complexity Basics

K. Subramani1

1Lane Department of Computer Science and Electrical EngineeringWest Virginia University

February 2, 2015

Basics Computational Complexity

Outline

1 Review of concepts

2 Problems and Solutions

3 Time, Space and Scaling

4 Intrinsic Complexity

5 Polynomial Time

Outline

5 Polynomial Time

Outline

5 Polynomial Time

Outline

5 Polynomial Time

Outline

5 Polynomial Time

Review of conceptsProblems and Solutions

Time, Space and ScalingIntrinsic Complexity

Polynomial Time

Review

Main concepts

1 Alphabet, strings and languages.2 Problems.3 Connection between problems and languages.4 Asymptotics and caclulus.5 Probability and random variables.6 Abstract Algebra.7 Upper and lower bounds.8 Problem paradigms.

Polynomial Time

Review

Main concepts

Polynomial Time

Review

Main concepts

1 Alphabet, strings and languages.

2 Problems.3 Connection between problems and languages.4 Asymptotics and caclulus.5 Probability and random variables.6 Abstract Algebra.7 Upper and lower bounds.8 Problem paradigms.

Polynomial Time

Review

Main concepts

1 Alphabet, strings and languages.2 Problems.

3 Connection between problems and languages.4 Asymptotics and caclulus.5 Probability and random variables.6 Abstract Algebra.7 Upper and lower bounds.8 Problem paradigms.

Polynomial Time

Review

Main concepts

1 Alphabet, strings and languages.2 Problems.3 Connection between problems and languages.

4 Asymptotics and caclulus.5 Probability and random variables.6 Abstract Algebra.7 Upper and lower bounds.8 Problem paradigms.

Polynomial Time

Review

Main concepts

1 Alphabet, strings and languages.2 Problems.3 Connection between problems and languages.4 Asymptotics and caclulus.

5 Probability and random variables.6 Abstract Algebra.7 Upper and lower bounds.8 Problem paradigms.

Polynomial Time

Review

Main concepts

1 Alphabet, strings and languages.2 Problems.3 Connection between problems and languages.4 Asymptotics and caclulus.5 Probability and random variables.

6 Abstract Algebra.7 Upper and lower bounds.8 Problem paradigms.

Polynomial Time

Review

Main concepts

1 Alphabet, strings and languages.2 Problems.3 Connection between problems and languages.4 Asymptotics and caclulus.5 Probability and random variables.6 Abstract Algebra.

7 Upper and lower bounds.8 Problem paradigms.

Polynomial Time

Review

Main concepts

1 Alphabet, strings and languages.2 Problems.3 Connection between problems and languages.4 Asymptotics and caclulus.5 Probability and random variables.6 Abstract Algebra.7 Upper and lower bounds.

8 Problem paradigms.

Polynomial Time

Review

Main concepts

Polynomial Time

Problem

Main points

1 Instance of a problem e.g., Eulerian tour.2 Finiteness of instance.3 The Eulerian path problem.4 The Hamilton circuit problem.5 The Chess problem.6 Infinite set of instances.7 Decision and search problems.

Polynomial Time

Problem

Main points

Polynomial Time

Problem

Main points

1 Instance of a problem e.g., Eulerian tour.

2 Finiteness of instance.3 The Eulerian path problem.4 The Hamilton circuit problem.5 The Chess problem.6 Infinite set of instances.7 Decision and search problems.

Polynomial Time

Problem

Main points

1 Instance of a problem e.g., Eulerian tour.2 Finiteness of instance.

3 The Eulerian path problem.4 The Hamilton circuit problem.5 The Chess problem.6 Infinite set of instances.7 Decision and search problems.

Polynomial Time

Problem

Main points

1 Instance of a problem e.g., Eulerian tour.2 Finiteness of instance.3 The Eulerian path problem.

4 The Hamilton circuit problem.5 The Chess problem.6 Infinite set of instances.7 Decision and search problems.

Polynomial Time

Problem

Main points

1 Instance of a problem e.g., Eulerian tour.2 Finiteness of instance.3 The Eulerian path problem.4 The Hamilton circuit problem.

5 The Chess problem.6 Infinite set of instances.7 Decision and search problems.

Polynomial Time

Problem

Main points

1 Instance of a problem e.g., Eulerian tour.2 Finiteness of instance.3 The Eulerian path problem.4 The Hamilton circuit problem.5 The Chess problem.

6 Infinite set of instances.7 Decision and search problems.

Polynomial Time

Problem

Main points

1 Instance of a problem e.g., Eulerian tour.2 Finiteness of instance.3 The Eulerian path problem.4 The Hamilton circuit problem.5 The Chess problem.6 Infinite set of instances.

7 Decision and search problems.

Polynomial Time

Problem

Main points

Polynomial Time

Solutions

Main points

1 What is a solution?2 What is an algorithm?3 Algorithms as functions.

Polynomial Time

Solutions

Main points

Polynomial Time

Solutions

Main points

1 What is a solution?

2 What is an algorithm?3 Algorithms as functions.

Polynomial Time

Solutions

Main points

1 What is a solution?2 What is an algorithm?

3 Algorithms as functions.

Polynomial Time

Solutions

Main points

Polynomial Time

The GCD problem

Definition

Given two positive numbers (integers) a and b, find their greatest common divisor.

1 This problem is referred to as gcd(a, b).2 It is also well-defined when a and b are not positive, as long as both are not zero.3 Euclid observed that gcd(a, b) = gcd(b, a mod b).

Polynomial Time

The GCD problem

Definition

Polynomial Time

The GCD problem

Definition

Polynomial Time

The GCD problem

Definition

Polynomial Time

The GCD problem

Definition

Polynomial Time

The GCD problem

Definition

1 This problem is referred to as gcd(a, b).

2 It is also well-defined when a and b are not positive, as long as both are not zero.3 Euclid observed that gcd(a, b) = gcd(b, a mod b).

Polynomial Time

The GCD problem

Definition

1 This problem is referred to as gcd(a, b).2 It is also well-defined when a and b are not positive,

as long as both are not zero.3 Euclid observed that gcd(a, b) = gcd(b, a mod b).

Polynomial Time

The GCD problem

Definition

1 This problem is referred to as gcd(a, b).2 It is also well-defined when a and b are not positive, as long as both are not zero.

3 Euclid observed that gcd(a, b) = gcd(b, a mod b).

Polynomial Time

The GCD problem

Definition

1 This problem is referred to as gcd(a, b).2 It is also well-defined when a and b are not positive, as long as both are not zero.3 Euclid observed that gcd(a, b) =

gcd(b, a mod b).

Polynomial Time

The GCD problem

Definition

Polynomial Time

The Euclidean Algorithm

Algorithm

1: FUNCTION GCD(a, b)2: if (b = 0) then3: return (a)4: else5: return (GCD(b, a mod b)).6: end if

Polynomial Time

The Euclidean Algorithm

Algorithm

1: FUNCTION GCD(a, b)2: if (b = 0) then3: return (a)4: else5: return (GCD(b, a mod b)).6: end if

Polynomial Time

Types of Analysis

How to analyze

1 Worst-case.2 Adversary.3 Average-case.4 Other ways.

Polynomial Time

Types of Analysis

How to analyze

Polynomial Time

Types of Analysis

How to analyze

1 Worst-case.

2 Adversary.3 Average-case.4 Other ways.

Polynomial Time

Types of Analysis

How to analyze

1 Worst-case.2 Adversary.

3 Average-case.4 Other ways.

Polynomial Time

Types of Analysis

How to analyze

1 Worst-case.2 Adversary.3 Average-case.

4 Other ways.

Polynomial Time

Types of Analysis

How to analyze

Polynomial Time

Time, Space and Scaling

Main Parameters

1 Notion of time.2 Notion of space.3 Notion of input size.4 Scaling with respect to input size.5 Analysis of the Euclidean algorithm.

Polynomial Time

Main Parameters

Polynomial Time

Main Parameters

1 Notion of time.

2 Notion of space.3 Notion of input size.4 Scaling with respect to input size.5 Analysis of the Euclidean algorithm.

Polynomial Time

Main Parameters

1 Notion of time.2 Notion of space.

3 Notion of input size.4 Scaling with respect to input size.5 Analysis of the Euclidean algorithm.

Polynomial Time

Main Parameters

1 Notion of time.2 Notion of space.3 Notion of input size.

4 Scaling with respect to input size.5 Analysis of the Euclidean algorithm.

Polynomial Time

Main Parameters

1 Notion of time.2 Notion of space.3 Notion of input size.4 Scaling with respect to input size.

5 Analysis of the Euclidean algorithm.

Polynomial Time

Main Parameters

Polynomial Time

Intrinsic Complexity

Definition

The intrinsic complexity of a problem is the complexity of the most efficient algorithmthat solves it.

Polynomial Time

Definition

Polynomial Time

Definition

Polynomial Time

The integer multiplication problem

Two integer multiplication

Compute the product of two numbers a and b having n digits each.

Polynomial Time

The Master Method for solving certain recurrences

The Master theorem

Consider the recurrenceT (n) = a · T (

) + f (n).

If f (n) ∈ O(nc), where c < logb a, then T (n) = Θ(nlogb a).

Polynomial Time

The Master theorem

) + f (n).

Polynomial Time

The Master theorem

Consider the recurrence

T (n) = a · T (nb

) + f (n).

Polynomial Time

The Master theorem

) + f (n).

Polynomial Time

The Master theorem

) + f (n).

If f (n) ∈ O(nc), where c < logb a,

then T (n) = Θ(nlogb a).

Polynomial Time

The Master theorem

) + f (n).

Polynomial Time

The Grade school approach

Grade school algorithm

1 Multiply a with each digit of b performing appropriate shifting.2 Add up the individual products.

The above algorithm requires Θ(n2) multiplications and Θ(n2) additions.

Polynomial Time

1 Multiply a with each digit of b performing appropriate shifting.

2 Add up the individual products.

Polynomial Time

The above algorithm requires

Θ(n2) multiplications and Θ(n2) additions.

Polynomial Time

The above algorithm requires Θ(n2) multiplications and

Θ(n2) additions.

Polynomial Time

An improved algorithm

A slightly more sophisticated approach

1 Let a = 〈x , y〉 and b = 〈u, v〉, where x and u are the first n2 digits of the numbers

a and b respectively.

2 It follows that a = 10n2 · x + y and b = 10

n2 · u + v .

3 Hence, a · b = 10n · x · u + 10n2 · (x · v + y · u) + y · v .

4 Recall that in the decimal algebra, multiplying by powers of 10 is tantamount to leftshifting.

5 Thus, if T (n) is the time taken to multiply two integers, then

T (n) = 4 · T (n2

) + O(n)

6 Solving the above recurrence gives T (n) = Θ(n2).

Polynomial Time

n2 · u + v .

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

1 Let a = 〈x , y〉 and b = 〈u, v〉,

where x and u are the first n2 digits of the numbers

n2 · u + v .

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

n2 · u + v .

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

n2 · u + v .

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

n2 · u + v .

3 Hence, a · b =

10n · x · u + 10n2 · (x · v + y · u) + y · v .

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

n2 · u + v .

3 Hence, a · b = 10n · x · u

+ 10n2 · (x · v + y · u) + y · v .

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

n2 · u + v .

3 Hence, a · b = 10n · x · u + 10n2 · (x · v + y · u)

+ y · v .4 Recall that in the decimal algebra, multiplying by powers of 10 is tantamount to left

shifting.5 Thus, if T (n) is the time taken to multiply two integers, then

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

n2 · u + v .

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

n2 · u + v .

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

n2 · u + v .

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

n2 · u + v .

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

n2 · u + v .

T (n) = 4 · T (n2

) + O(n)

6 Solving the above recurrence gives T (n) =

Θ(n2).

Polynomial Time

n2 · u + v .

T (n) = 4 · T (n2

) + O(n)

Polynomial Time

An asymptotically superior algorithm

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

1 Compute x · u, y · v and (x + y) · (u + v).2 Now compute (x · v + y · u), using the above observation.

3 Finally, compute a · b = 10n · x · u + 10n2 · (x · v + y · u) + y · v .

Analysis

T (n) = 3 · T (n2

) + O(n)

It follows that T (n) = O(nlog2 3).

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v =

x · v + y · u

New algorithm

Analysis

T (n) = 3 · T (n2

) + O(n)

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

Analysis

T (n) = 3 · T (n2

) + O(n)

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

Analysis

T (n) = 3 · T (n2

) + O(n)

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

1 Compute x · u, y · v and (x + y) · (u + v).

2 Now compute (x · v + y · u), using the above observation.

Analysis

T (n) = 3 · T (n2

) + O(n)

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

1 Compute x · u, y · v and (x + y) · (u + v).2 Now compute (x · v + y · u),

using the above observation.

Analysis

T (n) = 3 · T (n2

) + O(n)

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

Analysis

T (n) = 3 · T (n2

) + O(n)

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

3 Finally, compute a · b =

10n · x · u + 10n2 · (x · v + y · u) + y · v .

Analysis

T (n) = 3 · T (n2

) + O(n)

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

3 Finally, compute a · b = 10n · x · u

+ 10n2 · (x · v + y · u) + y · v .

Analysis

T (n) = 3 · T (n2

) + O(n)

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

3 Finally, compute a · b = 10n · x · u + 10n2 · (x · v + y · u)

+ y · v .

Analysis

T (n) = 3 · T (n2

) + O(n)

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

Analysis

T (n) = 3 · T (n2

) + O(n)

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

Analysis

T (n) =

3 · T (n2

) + O(n)

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

Analysis

T (n) = 3 · T (n2

) + O(n)

It follows that T (n) =

O(nlog2 3).

Polynomial Time

A neat observation

(x + y) · (u + v)− x · u − y · v = x · v + y · u

New algorithm

Analysis

T (n) = 3 · T (n2

) + O(n)

Polynomial Time

Polynomial versus Exponential

When is computation infeasible?

1 Difference between Θ(n) and Θ(2n) when n doubles.2 n! when n = 20 is larger than the date of the universe.3 The Vishnu Sharma story.

Polynomial Time

1 Difference between Θ(n) and Θ(2n) when n doubles.

2 n! when n = 20 is larger than the date of the universe.3 The Vishnu Sharma story.

Polynomial Time

1 Difference between Θ(n) and Θ(2n) when n doubles.2 n! when n = 20 is larger than the date of the universe.

3 The Vishnu Sharma story.

Polynomial Time

Analyzing Euler’s Algorithm

Details

1 How fast is Euler’s algorithm? Details!2 How many times does the main for loop run?3 Measuring running time. What is an elementary step?4 How much time to measure the degree of a vertex? Representation details.5 Tape storage versus RAM storage.6 Size of instance.

Polynomial Time

Details

Polynomial Time

Details

1 How fast is Euler’s algorithm?

Details!2 How many times does the main for loop run?3 Measuring running time. What is an elementary step?4 How much time to measure the degree of a vertex? Representation details.5 Tape storage versus RAM storage.6 Size of instance.

Polynomial Time

Details

1 How fast is Euler’s algorithm? Details!

2 How many times does the main for loop run?3 Measuring running time. What is an elementary step?4 How much time to measure the degree of a vertex? Representation details.5 Tape storage versus RAM storage.6 Size of instance.

Polynomial Time

Details

1 How fast is Euler’s algorithm? Details!2 How many times does the main for loop run?

3 Measuring running time. What is an elementary step?4 How much time to measure the degree of a vertex? Representation details.5 Tape storage versus RAM storage.6 Size of instance.

Polynomial Time

Details

1 How fast is Euler’s algorithm? Details!2 How many times does the main for loop run?3 Measuring running time.

What is an elementary step?4 How much time to measure the degree of a vertex? Representation details.5 Tape storage versus RAM storage.6 Size of instance.

Polynomial Time

Details

1 How fast is Euler’s algorithm? Details!2 How many times does the main for loop run?3 Measuring running time. What is an elementary step?

4 How much time to measure the degree of a vertex? Representation details.5 Tape storage versus RAM storage.6 Size of instance.

Polynomial Time

Details

1 How fast is Euler’s algorithm? Details!2 How many times does the main for loop run?3 Measuring running time. What is an elementary step?4 How much time to measure the degree of a vertex?

Representation details.5 Tape storage versus RAM storage.6 Size of instance.

Polynomial Time

Details

1 How fast is Euler’s algorithm? Details!2 How many times does the main for loop run?3 Measuring running time. What is an elementary step?4 How much time to measure the degree of a vertex? Representation details.

5 Tape storage versus RAM storage.6 Size of instance.

Polynomial Time

Details

1 How fast is Euler’s algorithm? Details!2 How many times does the main for loop run?3 Measuring running time. What is an elementary step?4 How much time to measure the degree of a vertex? Representation details.5 Tape storage versus RAM storage.

6 Size of instance.

Polynomial Time

Details

Polynomial Time

The class P

Definition

P is the class of problems for which an algorithm exists that solves problems of size n,in time O(nc) for some constant c.

1 P is a complexity class.2 A problem is not in P, if . . ...3 We include both decision and search problems.4 In general, TIME(f(n)) is the class of problems for which an algorithm exists that

solves problems of size n, in time TIME(f(n)).5 P = ∪c>oTIME(nc).6 Is TIME(nlog n) ⊆ P?7 Defining TIME(f (n)).

8 The class EXP is defined as TIME(2nc).

Polynomial Time

The class P

Definition

Polynomial Time

The class P

Definition

Polynomial Time

The class P

Definition

Polynomial Time

The class P

Definition

1 P is a complexity class.

2 A problem is not in P, if . . ...3 We include both decision and search problems.4 In general, TIME(f(n)) is the class of problems for which an algorithm exists that

Polynomial Time

The class P

Definition

1 P is a complexity class.2 A problem is not in P, if . . ...

3 We include both decision and search problems.4 In general, TIME(f(n)) is the class of problems for which an algorithm exists that

Polynomial Time

The class P

Definition

1 P is a complexity class.2 A problem is not in P, if . . ...3 We include both decision and search problems.

4 In general, TIME(f(n)) is the class of problems for which an algorithm exists thatsolves problems of size n, in time TIME(f(n)).

5 P = ∪c>oTIME(nc).6 Is TIME(nlog n) ⊆ P?7 Defining TIME(f (n)).

Polynomial Time

The class P

Definition

solves problems of size n, in time TIME(f(n)).

5 P = ∪c>oTIME(nc).6 Is TIME(nlog n) ⊆ P?7 Defining TIME(f (n)).

Polynomial Time

The class P

Definition

solves problems of size n, in time TIME(f(n)).5 P = ∪c>oTIME(nc).

6 Is TIME(nlog n) ⊆ P?7 Defining TIME(f (n)).

Polynomial Time

The class P

Definition

solves problems of size n, in time TIME(f(n)).5 P = ∪c>oTIME(nc).6 Is TIME(nlog n) ⊆ P?

7 Defining TIME(f (n)).

Polynomial Time

The class P

Definition

Polynomial Time

The class P

Definition

Polynomial Time

Robustness of P

Why P is such a robust class

1 Elementary steps can change within reason.2 Architecture can change within reason.3 Input formats can change within reason.Binary/unary encoding. Buffing up the

input.4 Implementation details can change within reason.

Being in P is a fundamental property of a problem and not dependent upon howsomebody goes about solving it.

Polynomial Time

Robustness of P

Polynomial Time

Robustness of P

1 Elementary steps can change within reason.

2 Architecture can change within reason.3 Input formats can change within reason.Binary/unary encoding. Buffing up the

Polynomial Time

Robustness of P

1 Elementary steps can change within reason.2 Architecture can change within reason.

3 Input formats can change within reason.Binary/unary encoding. Buffing up theinput.

4 Implementation details can change within reason.

Polynomial Time

Robustness of P

input.

4 Implementation details can change within reason.

Polynomial Time

Robustness of P

Polynomial Time

Robustness of P

Polynomial Time

Tractability and mathematical insight

What is tractable

1 Definition of tractability.2 Does tractability coincide with P?3 How practical is O(n10)?4 Sublinear algorithms.5 Going from Pc to P gives fundamental insight into the nature of problems.

Polynomial Time

What is tractable

Polynomial Time

What is tractable

1 Definition of tractability.

2 Does tractability coincide with P?3 How practical is O(n10)?4 Sublinear algorithms.5 Going from Pc to P gives fundamental insight into the nature of problems.

Polynomial Time

What is tractable

1 Definition of tractability.2 Does tractability coincide with P?

3 How practical is O(n10)?4 Sublinear algorithms.5 Going from Pc to P gives fundamental insight into the nature of problems.

Polynomial Time

What is tractable

1 Definition of tractability.2 Does tractability coincide with P?3 How practical is O(n10)?

4 Sublinear algorithms.5 Going from Pc to P gives fundamental insight into the nature of problems.

Polynomial Time

What is tractable

1 Definition of tractability.2 Does tractability coincide with P?3 How practical is O(n10)?4 Sublinear algorithms.

5 Going from Pc to P gives fundamental insight into the nature of problems.

Polynomial Time

What is tractable

Complexity Basics - West Virginia...

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Plates and Shells - West Virginia Universitycommunity.wvu.edu/~bpbettig/MAE456/Lecture_10_Shell_Elements.pdf · Plates and Shells All images are from ... Thin Plate Formulation •

Lecture 2 Computational Complexity Time Complexity Space Complexity.