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UMass Lowell Computer Science 91.503 Analysis

of Algorithms Prof. Karen Daniels Fall, 2006

- Lecture 6
- Wednesday, 10/11/06
- NP-Completeness

Overview

- NP-Completeness, Part 1
- Graham videotape
- Review Reduction to Establish Lower Bounds
- Basics
- NP-Completeness, Part 2
- More basics
- How to Treat NP- Hard or Complete Problems

Resources

- Recommended
- Computers Intractability
- by Garey Johnson
- W.H.Freeman
- 1990
- ISBN 0716710455.

Motivation

Why do we care??

- When faced with a problem in practice, need to

know whether or not one can expect produce a

fast algorithm (i.e. O(nk)) to solve it - Intractable problems occur frequently in

practice - Surface differences between tractable and

intractable problems are often hard to see - e.g. MST solvable in polynomial time but

Dominating Set intractable

Dominating Set

source Garey Johnson

Industrial MotivationSupporting Apparel

Manufacturing

Covering Research Example Translational 2D

Covering CCCG01, CCCG03 conference papers

- Input
- Covering polygons Q Q1, Q2 , ... , Qm
- Target polygons (or point-sets) P P1, P2 , ...

, Pn - Output
- Translations g g 1, g 2 , ... , g m such that

STATIC

NP-COMPLETE

Covering Problem Taxonomy

covering

non-geometric covering

geometric covering

VERTEX-COVER, SET-COVER, EDGE-COVER, VLSI logic

minimization, facility location

2D translational covering

P finite point sets

P shapes

Q identical

- Thin coverings of the plane with congruent convex

shapes - Translational covering of a convex set by a

sequence of convex shapes - Translational covering of nonconvex set with

nonidentical covering shapes

Q nonconvex

Q convex

1D interval covered by annuli

BOX-COVER

partition

- NP-hard/complete polygon problems
- polynomial-time results for restricted orthogonal

polygon covering and horizontally convex polygons - approximation algorithms for boundary, corner

covers of orthogonal polygons

Polynomial-time algorithms for triangulation and

some tilings

Reduction to Establish Lower Bound

- Tightening a Lower Bound for Maximum-Area

Axis-Parallel Rectangle Problem - from W(n) to W(nlgn)

Approach

- Go around lower bound brick wall by
- examining strong lower bounds for some similar

problems - transforming a similar problem to the rectangle

problem - this process is similar to what we do when we

prove problems NP-complete

worst-case bounds on problem

n5

2n

1

n2

n log2 n

n

Lower Bound of W(n log n) by Transforming a

(seemingly unrelated) Problem

MAX-GAP instance given n real numbers x1, x2,

... xn find the maximum difference between 2

consecutive numbers in the sorted list.

O(n) time transformation

Rectangle area is a solution to the MAX-GAP

instance

specialized polygon

Rectangle algorithm must take as least as much

time as MAX-GAP. MAX-GAP is known to be in W(n

log n).

Rectangle algorithm must take W(n log n) time for

specialized polygons. Transforming yet another

different problem yields bound for unspecialized

polygons.

NP-Completeness

Chapter 34

Basic Concepts

- Polynomial Time Problem Formalization
- Polynomial-Time Verification
- NP-Completeness Reducibility
- NP-Completeness Proofs
- Expanding List of Hard Problems via Reduction

Polynomial Time Problem Formalization

- Formalize notions of abstract and concrete

decision problems - Encoding a problem
- Define class of polynomial-time solvable decision

problems - Define complexity class
- Express relationship between decision problems

and algorithms that solve them using framework of

formal language theory

Polynomial Time Problem Formalization

(continued)

- Solvable in Polynomial Time
- i.e. O(nk) for some positive constant k
- Tractable k is typically small (e.g. 1, 2, 3)
- Typically holds across sequential computation

models - Closure properties (chaining/composition)
- Abstract Problem
- Binary relation on set of problem instances

solutions - Decision Problem yes/no answer
- impose bound on optimization problem to recast
- e.g. Dominating Set

Dominating Set

source Garey Johnson

Polynomial Time Problem Formalization

(continued)

- Encoding of set S of abstract objects is mapping

e from S to set of binary strings - Concrete Problem instance set is set of binary

strings - Concrete Problem is Polynomial-Time Solvable if

there exists an algorithm to solve it in time

O(nk) for some positive constant k - Complexity Class P set of polynomial-time

solvable concrete decision problems - Given abstract decision problem Q that maps

instance set I to 0,1 can induce a

concrete decision problem e(Q) - If solution to abstract-problem instance

- then solution to concrete-problem instance

is also Q(i)

Polynomial Time Problem Formalization

(continued)

Goal Extend definition of polynomial-time

solvability from concrete to abstract using

encodings as bridge

- is polynomial-time

computable if there exists a polynomial-time

algorithm A that, given any input

, produces f(x) as output. - For set I of problem instances, encodings are

polynomially related if there exist 2

polynomial-time computable functions f12, f21

such that - If 2 encodings e1, e2 of abstract problem are

polynomially related, then polynomial-time

solvability of problem is encoding-independent

Polynomial Time Problem Formalization

(continued)

- Formal Language Framework
- Alphabet S is finite set of symbols
- Language L over S is any set of strings

consisting of symbols from S - Set of instances for decision problem Q is
- View Q as language L over S 0,1
- Express relation between decision problems and

algorithms - Algorithm A accepts string if,

given input x, output A(x) is 1 - Language accepted by A is set of strings A accepts

Polynomial Time Problem Formalization

(continued)

- Formal Language Framework (continued)
- Language L is decided by algorithm A if every

binary string in L is accepted by A and every

binary string not in L is rejected by A - Language L is accepted in polynomial time by

algorithm A if it is accepted by A and if exists

constant k such that, for any length-n string

, A accepts x in time O(nk) - Language L is decided in polynomial time by

algorithm A if exists constant k such that, for

any length-n string , A

correctly decides in time O(nk) if

Polynomial Time Problem Formalization

(continued)

- Formal Language Framework (continued)
- Complexity Class is a set of languages whose

membership is determined by a complexity measure

(i.e. running time) of algorithm that determines

whether a given string belongs to a language. - We already defined Complexity Class P set of

polynomial-time solvable concrete decision

problems - Language Framework gives other definitions

P there exists an algorithm A that

decides L in polynomial time

L L is accepted by a polynomial-time algorithm

Polynomial-Time Verification

- Examine algorithms that verify membership in

languages - Define class of decision problems whose solutions

can be verified in polynomial time

Polynomial-Time Verification (continued)

- Verification Algorithms
- algorithm A verifies language L if for any string

certificate y that A

can use to prove that - also, for any string certificate y that A can

use to prove that - language verified by verification algorithm A is

Polynomial-Time Verification (continued)

- The Complexity Class NP
- languages that can be verified by a

polynomial-time algorithm A - A verifies language L in polynomial time
- The Complexity Class co-NP
- set of languages L such that

Polynomial-Time Verification (continued)

P

P

P

34.3

source 91.503 textbook Cormen et al.

NP-Completeness Reducibility

- Polynomial-Time Reducibility
- Define complexity class of NP-complete problems
- Relationships among complexity classes
- Circuit satisfiability as an NP-complete problem

NP-Completeness Reducibility (continued)

- Q can be reduced to Q if any instance of Q can

be easily rephrased as an instance of Q, the

solution to which provides solution to instance

of Q - L1 is polynomial-time reducible to L2

if exists polynomial-time computable function

34.4

source 91.503 textbook Cormen et al.

NP-Completeness Reducibility (continued)

34.5

34.3

source 91.503 textbook Cormen et al.

NP-Completeness Reducibility (continued)

Theorem - If any NP-complete problem is

polynomial-time solvable, then PNP. -

Equivalently, if any problem in NP is not

polynomial-time solvable, then no NP-complete

problem is polynomial-time solvable.

source 91.503 textbook Cormen et al.

Expanding List of Hard Problems via Reduction

- Relationships among some NP-complete problems

Circuit-SAT

source Garey Johnson

source 91.503 textbook Cormen et al.

Need to show that starting problem is in NP

NP-Completeness Reducibility (continued)

Circuit Satisfiability

circuit inputs

- Satisfying Assignment truth assignment inducing

output 1 - GOAL Show Circuit Satisfiability is NP-complete

Language Def CIRCUIT-SAT ltCgtC is a

satisfiable boolean combinational circuit.

34.8

NP-Completeness Reducibility (continued)

Circuit Satisfiability

- CIRCUIT-SAT is in NP
- Construct 2-input polynomial-time algorithm A

that verifies CIRCUIT-SAT - Input 1 encoding of circuit C
- Input 2 certificate assignment of boolean

values to all wires of C - Algorithm A
- For each logic gate A checks if certificate value

for gates output wire correctly computes gates

function based on input wire values - If output of entire circuit is 1, algorithm

outputs 1 - Otherwise, algorithm outputs 0
- For satisfiable circuit, there is a

(polynomial-length) certificate that causes A to

output 1 - For unsatisfiable circuit, no certificate can

cause A to output 1 - Algorithm A runs in polynomial time

source 91.503 textbook Cormen et al.

NP-Completeness Reducibility (continued)

Circuit Satisfiability

- CIRCUIT-SAT is NP-hard
- L represents some language in NP
- Create polynomial-time algorithm F computing

reduction function f that maps every binary

string x to a circuit Cf(x) such that - Must exist algorithm A verifying L in polynomial

time. (A from NP) - F uses A to compute f.
- Represent computation of A as sequence of

configurations - F constructs single circuit that computes all

configurations produced by initial configuration

source 91.503 textbook Cormen et al.

NP-Completeness Proofs (continued)

- Proving a Language NP-Complete
- Proving a Language NP-Hard
- all steps except (1)

source 91.503 textbook Cormen et al.

NP-Completeness Proofs (continued)

- Reducing Boolean Circuit Satisfiability to

Boolean Formula Satisfiability - Boolean Formula Satisfiability Instance of

language SAT is a boolean formula f consisting

of - n boolean variables x1, x2, ... , xn
- m boolean connectives boolean function with 1 or

2 inputs and 1 output - e.g. AND, OR, NOT, implication, iff
- parentheses
- truth, satisfying assignments notions apply

source 91.503 textbook Cormen et al.

NP-Completeness Proofs (continued)

- Show SAT is NP-Complete
- SAT is in NP via argument similar to circuit case
- Reduce Boolean Circuit Satisfiability to Boolean

Formula Satisfiability - Careful! Naive approach might have shared

subformulas and cause formula size to grow

exponentially!

source 91.503 textbook Cormen et al.

NP-Completeness Proofs (continued)

- Show SAT is NP-Complete (continued)
- Reduce Boolean Circuit Satisfiability to Boolean

Formula Satisfiability (continued) - Approach For each wire xi in circuit C, formula

f has variable xi. Express gate operation.

34.10

source 91.503 textbook Cormen et al.

NP-Completeness Proofs (continued)

- Reducing Formula Satisfiability to

3-CNF-Satisfiability - Boolean Formula Satisfiability Instance of

language SAT is a boolean formula f - CNF conjunctive normal form
- conjunction AND of clauses
- clause OR of literal(s)
- 3-CNF each clause has exactly 3 distinct

literals - Show 3-CNF is NP-Complete

source 91.503 textbook Cormen et al.

NP-Completeness Proofs (continued)

- Show 3-CNF is NP-Complete (continued)
- 3CNF is in NP via argument similar to circuit

case - Reduce Formula Satisfiability to

3-CNF-Satisfiability - 3 steps progressively transforming formula f

closer to 3CNF - 1) Similar to reduction

Binary ParseTree

34.11

source 91.503 textbook Cormen et al.

NP-Completeness Proofs (continued)

- Show 3-CNF is NP-Complete (continued)
- 3CNF is in NP via argument similar to circuit

case - Reduce Formula Satisfiability to

3-CNF-Satisfiability - 3 steps progressively transforming formula f

closer to 3CNF - 2) Convert each clause fi into conjunctive

normal form - construct truth table for fi
- using entries for 0, build DNF formula for
- convert DNF into CNF formula fi by applying

DeMorgans laws - each clause has at most 3 literals

source 91.503 textbook Cormen et al.

NP-Completeness Proofs (continued)

- Show 3-CNF is NP-Complete (continued)
- 3CNF is in NP via argument similar to circuit

case - Reduce Formula Satisfiability to

3-CNF-Satisfiability - 3 steps progressively transforming formula f

closer to 3CNF - 3) Convert each clause fi so it has exactly 3

distinct literals - add padding variables
- create fi
- Size of resulting formula is polynomial in length

of original formula and reduction takes only

polynomial time and 3-CNF formula

source 91.503 textbook Cormen et al.

Expanding List of Hard Problems via Reduction

- Relationships among some NP-complete problems

Circuit-SAT

source Garey Johnson

source 91.503 textbook Cormen et al.

Vertex Cover

A

B

vertex cover of size 2

Vertex Cover of an undirected graph G(V,E) is a

subset

F

Dominating Set

source Garey Johnson

Clique

Clique of an undirected graph G(V,E) is a

complete subgraph of G.

clique of size 3

source Garey Johnson

Hamiltonian Cycle

34.2

Hamiltonian Cycle of an undirected graph G(V,E)

is a simple cycle that contains each vertex in V.

source 91.503 textbook Cormen et al.

Reducing 3-CNF-SAT to Clique

source 91.503 textbook Cormen et al.

Construct graph G such that f is satisfiable iff

G has clique of size k

x1

x3

x1

x2

x2

34.14

Reducing Clique to VertexCover

Construct complement G (V, E ) of graph G such

that G has clique of size k iff graph G has a

vertex cover of size V-k.

34.15

source 91.503 textbook Cormen et al.

How to Treat

NP-Hard or Complete Problems

How to Treat NP-Complete Problems??

- Approximation Algorithms
- Heuristic Upper or Lower Bounds
- Greedy, Simulated Annealing, Genetic Alg, AI
- Mathematical Programming
- Linear Programming for part of problem
- Integer Programming
- Quadratic Programming...
- Search Space Exploration
- Gradient Descent, Local Search, Pruning,

Subdivision - Randomization, Derandomization
- Leverage/Impose Problem Structure
- Leverage Similar Problems

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