Notations. For any hypergraph H 2 V we have. H + H = and H + H = 2 V.

Size: px
Start display at page:

Download "Notations. For any hypergraph H 2 V we have. H + H = and H + H = 2 V."

Transcription

1 Notations For any hypergraph H 2 V we have H + H = and H + H = 2 V.

2 Notations For any hypergraph H 2 V we have H + H = and H + H = 2 V. Given a Sperner hypergraph H, we can view it as the family of minimal sets in the monotone system H +. Similarly, H can be viewed as the family of maximal independent sets in the independence system H.

3 Notations For any hypergraph H 2 V we have H + H = and H + H = 2 V. Given a Sperner hypergraph H, we can view it as the family of minimal sets in the monotone system H +. Similarly, H can be viewed as the family of maximal independent sets in the independence system H. We shall assume that H or equivalently H are represented by a membership oracle Ω, either for the monotone system H + or for the independence system H (one is simply the negation of the other).

4 Notations For any hypergraph H 2 V we have H + H = and H + H = 2 V. Given a Sperner hypergraph H, we can view it as the family of minimal sets in the monotone system H +. Similarly, H can be viewed as the family of maximal independent sets in the independence system H. We shall assume that H or equivalently H are represented by a membership oracle Ω, either for the monotone system H + or for the independence system H (one is simply the negation of the other). The set B(Ω) = H H is called the boundary of Ω (as well as of the independence system H and the monotone system H + ).

5 Easy Facts Fact 1. Consider an arbitrary algorithm A, which generates every Sperner family H by using only a membership oracle Ω for the monotone system H +. Then, for every hypergraph H such an algorithm must call the oracle at least B(Ω) = H + H times.

6 Easy Facts Fact 1. Consider an arbitrary algorithm A, which generates every Sperner family H by using only a membership oracle Ω for the monotone system H +. Then, for every hypergraph H such an algorithm must call the oracle at least B(Ω) = H + H times. Proof. For any B B(Ω) let Ω B be defined by { Ω(S) if S B, Ω B (S) = Ω(S) if S = B. Then, Ω B is the membership oracle of a monotone system corresponding to a different Sperner hypergraph, and thus algorithm A must distinguish the systems represented by Ω and Ω B, for any B B(Ω).

7 Easy Facts Cont d Corollary 1. A purely membership oracle based generation of H (or H ) will (anyway) generate both sets H H.

8 Easy Facts Cont d Corollary 1. A purely membership oracle based generation of H (or H ) will (anyway) generate both sets H H. Corollary 2. Generating the maximal independent sets max I of an independence system I represented by a membership oracle Ω is not simply NP-hard (cf. Lawler, Lenstra and Rinnooy Kan, 1980) but exponential, in worst case!!! Because, for infinite families of independence systems we have for any polynomial poly(). min ( 2 V \ I ) poly( max I, Ω )

9 Easy Facts Cont d Corollary 3. A purely membership oracle based generation of H can be efficient in total time only if H poly( H, Ω ) for some polynomial poly(). If such an inequality hold, we say that H is dual bounded.

10 Easy Facts Cont d Corollary 3. A purely membership oracle based generation of H can be efficient in total time only if H poly( H, Ω ) for some polynomial poly(). If such an inequality hold, we say that H is dual bounded. We do not know if there is a purely membership oracle based algorithm A which would generate in polynomial total time every dual bounded hypergraph. BUT We do know that there is one, which does that in quasipolynomial total time.

11 The Idea of Joint Generation (Bioch and Ibaraki, 1995, Gurvich and Khachiyan, 1998) Given the Sperner hypergraphs S H and Q H, we have S = H and Q = H

12 The Idea of Joint Generation (Bioch and Ibaraki, 1995, Gurvich and Khachiyan, 1998) Given the Sperner hypergraphs S H and Q H, we have S = H and Q = H Q = S

13 The Idea of Joint Generation (Bioch and Ibaraki, 1995, Gurvich and Khachiyan, 1998) Given the Sperner hypergraphs S H and Q H, we have S = H and Q = H Q = S S + Q = 2 V

14 The Idea of Joint Generation (Bioch and Ibaraki, 1995, Gurvich and Khachiyan, 1998) Given the Sperner hypergraphs S H and Q H, we have S = H and Q = H Q = S S + Q = 2 V The latter conditions do not depend on H or H!!

15 The Idea of Joint Generation (Bioch and Ibaraki, 1995, Gurvich and Khachiyan, 1998) Given the Sperner hypergraphs S H and Q H, we have S = H and Q = H Q = S S + Q = 2 V Dualization: Given the Sperner hypergraphs S H and Q H, the equality S + Q = 2 V can be tested, and if it does not hold, a new subset X 2 V \ (S + Q ) can be found in O(N o(log N ) time, where N = S + Q. (Fredman and Khachiyan, 1996)

16 Joint Generation (Bioch and Ibaraki, 1995, Gurvich and Khachiyan, 1998) Given an oracle Ω representing the monotone system H + 2 V independence system H 2 V ), let us start with S = Q =. (or the While S + Q 2 V do: Let X 2 V \ (S + Q ) be the set found by dualization. Test Ω(X) =? (remember, we have H + H = 2 V ). If X H +, then by calling the oracle at most X -times, find a set H H for which X H, and set S = S {H}. If X H, then by calling the oracle at most V \ X -times find a set H H for which X H, and set Q = Q {H}. The complexity of one incremental step is O(N o(log N ) + O(n Ω ) time, where N = S + Q and n = V.

17 Joint Generation (Bioch and Ibaraki, 1995, Gurvich and Khachiyan, 1998) Theorem. Given an oracle Ω representing a monotone system H +, joint generation generates the boundary family B(Ω) = H H in incremental quasi-polynomial time.

18 Joint Generation (Bioch and Ibaraki, 1995, Gurvich and Khachiyan, 1998) Theorem. Given an oracle Ω representing a monotone system H +, joint generation generates the boundary family B(Ω) = H H in incremental quasi-polynomial time. Consequence. If H is dual bounded, i.e. H poly( H, Ω ), then joint generation generates H in quasi-polynomial total time (just do not output sets in H ).

19 Joint Generation (Bioch and Ibaraki, 1995, Gurvich and Khachiyan, 1998) Theorem. Given an oracle Ω representing a monotone system H +, joint generation generates the boundary family B(Ω) = H H in incremental quasi-polynomial time. Consequence. If H is dual bounded, i.e. H poly( H, Ω ), then joint generation generates H in quasi-polynomial total time (just do not output sets in H ). Remark. Joint generation may not generate H incrementally efficiently. For instance, we may get all the sets of H before getting any from H!

20 Modifying Joint Generation Fact 2. If S H, Q H, and Q S, then for any Q Q \ S we have a j V \ Q such that X = Q {j} S +. For such a set we must have X H + \ S +.

21 Modifying Joint Generation Fact 2. If S H, Q H, and Q S, then for any Q Q \ S we have a j V \ Q such that X = Q {j} S +. For such a set we must have X H + \ S +. 1 V 1

22 Modifying Joint Generation Fact 2. If S H, Q H, and Q S, then for any Q Q \ S we have a j V \ Q such that X = Q {j} S +. For such a set we must have X H + \ S +. 1 V H + H 1

23 Modifying Joint Generation Fact 2. If S H, Q H, and Q S, then for any Q Q \ S we have a j V \ Q such that X = Q {j} S +. For such a set we must have X H + \ S +. 1 V S + S H + H 1

24 Modifying Joint Generation Fact 2. If S H, Q H, and Q S, then for any Q Q \ S we have a j V \ Q such that X = Q {j} S +. For such a set we must have X H + \ S +. 1 V S + S Q Q H + H 1

25 Modifying Joint Generation Fact 2. If S H, Q H, and Q S, then for any Q Q \ S we have a j V \ Q such that X = Q {j} S +. For such a set we must have X H + \ S +. 1 V Q S + S S Q Q H + H 1

26 Modifying Joint Generation Fact 2. If S H, Q H, and Q S, then for any Q Q \ S we have a j V \ Q such that X = Q {j} S +. For such a set we must have X H + \ S +. 1 V Q S + S 1 X S Q Q H + H 1

27 Modifying Joint Generation Fact 2. If S H, Q H, and Q S, then for any Q Q \ S we have a j V \ Q such that X = Q {j} S +. For such a set we must have X H + \ S +. The condition Q S can be tested, and if it holds, then a set X H + \S + can be constructed in O(n S Q ) time. We do not need the oracle Ω for this.

28 Modified Joint Generation While Q S, let X H + \ S + be constructed as above, and by calling the oracle at most X -times, find a set H H for which X H, and set S = S {H}. If Q S and S + Q 2 V then let X 2 V \ (S + Q ) be found by dualization. Test Ω(X) =? (remember, we have H + H = 2 V ). If X H +, then by calling the oracle at most X -times, find a set H H for which X H, and set S = S {H}. If X H, then by calling the oracle at most V \ X -times find a set H H for which X H, and set Q = Q {H}. Return to the while loop.

29 Uniformly Dual Bounded Hypergraphs Fact 2. In every iteration of the modified joint generation we have Q S H + 1. Corollary 4. Joint generation becomes incrementally efficient (quasipolynomial) if for all subfamilies S H the following inequality holds: S H poly( Ω, S ). Let us call a hypergraph H (represented by a membership oracle Ω) uniformly dual bounded if the above inequality holds for all subfamilies S H.

30 Uniformly Dual Bounded Hypergraphs Fact 2. In every iteration of the modified joint generation we have Q S H + 1. Corollary 4. Joint generation becomes incrementally efficient (quasipolynomial) if for all subfamilies S H the following inequality holds: S H poly( Ω, S ). Let us call a hypergraph H (represented by a membership oracle Ω) uniformly dual bounded if the above inequality holds for all subfamilies S H. Corollary 5. Since dualization is used as a black-box, joint generation may increment Q consecutively, up to S H before incrementing S. Thus, joint generation is incrementally efficient to generate H if and only if H is uniformly dual bounded.

Generating Partial and Multiple Transversals of a Hypergraph

Generating Partial and Multiple Transversals of a Hypergraph Generating Partial and Multiple Transversals of a Hypergraph Endre Boros 1, Vladimir Gurvich 2, Leonid Khachiyan 3, and Kazuhisa Makino 4 1 RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway

More information

On Generating All Minimal Integer Solutions for a Monotone System of Linear Inequalities

On Generating All Minimal Integer Solutions for a Monotone System of Linear Inequalities On Generating All Minimal Integer Solutions for a Monotone System of Linear Inequalities E. Boros 1, K. Elbassioni 2, V. Gurvich 1, L. Khachiyan 2, and K. Makino 3 1 RUTCOR, Rutgers University, 640 Bartholomew

More information

Dual-Bounded Generating Problems: Weighted Transversals of a Hypergraph

Dual-Bounded Generating Problems: Weighted Transversals of a Hypergraph Dual-Bounded Generating Problems: Weighted Transversals of a Hypergraph E. Boros V. Gurvich L. Khachiyan K. Makino January 15, 2003 Abstract We consider a generalization of the notion of transversal to

More information

An Intersection Inequality for Discrete Distributions and Related Generation Problems

An Intersection Inequality for Discrete Distributions and Related Generation Problems An Intersection Inequality for Discrete Distributions and Related Generation Problems E. Boros 1, K. Elbassioni 1, V. Gurvich 1, L. Khachiyan 2, and K. Makino 3 1 RUTCOR, Rutgers University, 640 Bartholomew

More information

Self-duality of bounded monotone boolean functions and related problems

Self-duality of bounded monotone boolean functions and related problems Discrete Applied Mathematics 156 (2008) 1598 1605 www.elsevier.com/locate/dam Self-duality of bounded monotone boolean functions and related problems Daya Ram Gaur a, Ramesh Krishnamurti b a Department

More information

DUAL-BOUNDED GENERATING PROBLEMS: PARTIAL AND MULTIPLE TRANSVERSALS OF A HYPERGRAPH

DUAL-BOUNDED GENERATING PROBLEMS: PARTIAL AND MULTIPLE TRANSVERSALS OF A HYPERGRAPH DUAL-BOUNDED GENERATING PROBLEMS: PARTIAL AND MULTIPLE TRANSVERSALS OF A HYPERGRAPH ENDRE BOROS, VLADIMIR GURVICH, LEONID KHACHIYAN, AND KAZUHISA MAKINO Abstract. We consider two natural generalizations

More information

Generating Maximal Independent Sets for Hypergraphs with Bounded Edge-Intersections

Generating Maximal Independent Sets for Hypergraphs with Bounded Edge-Intersections Generating Maximal Independent Sets for Hypergraphs with Bounded Edge-Intersections Endre Boros 1, Khaled Elbassioni 1, Vladimir Gurvich 1, and Leonid Khachiyan 2 1 RUTCOR, Rutgers University, 640 Bartholomew

More information

An Efficient Implementation of a Joint Generation Algorithm

An Efficient Implementation of a Joint Generation Algorithm An Efficient Implementation of a Joint Generation Algorithm E. Boros 1, K. Elbassioni 1, V. Gurvich 1, and L. Khachiyan 2 1 RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway, NJ 08854-8003,

More information

An Efficient Implementation of a Quasi-Polynomial Algorithm for Generating Hypergraph Transversals and its Application in Joint Generation

An Efficient Implementation of a Quasi-Polynomial Algorithm for Generating Hypergraph Transversals and its Application in Joint Generation An Efficient Implementation of a Quasi-Polynomial Algorithm for Generating Hypergraph Transversals and its Application in Joint Generation E. Boros K. Elbassioni V. Gurvich L. Khachiyan Abstract Given

More information

An inequality for polymatroid functions and its applications

An inequality for polymatroid functions and its applications An inequality for polymatroid functions and its applications E. Boros a K. Elbassioni b V. Gurvich a L. Khachiyan b a RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway NJ 08854-8003; ({boros,

More information

An Incremental RNC Algorithm for Generating All Maximal Independent Sets in Hypergraphs of Bounded Dimension

An Incremental RNC Algorithm for Generating All Maximal Independent Sets in Hypergraphs of Bounded Dimension An Incremental RNC Algorithm for Generating All Maximal Independent Sets in Hypergraphs of Bounded Dimension E. Boros K. Elbassioni V. Gurvich L. Khachiyan Abstract We show that for hypergraphs of bounded

More information

On the fractional chromatic number of monotone self-dual Boolean functions

On the fractional chromatic number of monotone self-dual Boolean functions Discrete Mathematics 309 (2009) 867 877 www.elsevier.com/locate/disc On the fractional chromatic number of monotone self-dual Boolean functions Daya Ram Gaur a,, Kazuhisa Makino b a University of Lethbridge,

More information

On the Complexity of Some Enumeration Problems for Matroids

On the Complexity of Some Enumeration Problems for Matroids On the Complexity of Some Enumeration Problems for Matroids Endre Boros Khaled Elbassioni Vladimir Gurvich Leonid Khachiyan Abstract We present an incremental polynomial-time algorithm for enumerating

More information

A Study on Monotone Self-Dual Boolean Functions

A Study on Monotone Self-Dual Boolean Functions A Study on Monotone Self-Dual Boolean Functions Mustafa Altun a and Marc D Riedel b a Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey 34469 b Electrical and Computer

More information

Contents. Typical techniques. Proving hardness. Constructing efficient algorithms

Contents. Typical techniques. Proving hardness. Constructing efficient algorithms Contents Typical techniques Proving hardness Constructing efficient algorithms Generating Maximal Independent Sets Consider the generation of all maximal sets of an independence system. Generating Maximal

More information

Some fixed-parameter tractable classes of Dual and related problems

Some fixed-parameter tractable classes of Dual and related problems Some fixed-parameter tractable classes of Dual and related problems Khaled Elbassioni 1, Matthias Hagen 2, Imran Rauf 1 1 Max-Planck-Institut für Informatik, D 66123 Saarbrücken, {elbassio,irauf}@mpi-inf.mpg.de

More information

ENUMERATING SPANNING AND CONNECTED SUBSETS IN GRAPHS AND MATROIDS y

ENUMERATING SPANNING AND CONNECTED SUBSETS IN GRAPHS AND MATROIDS y Journal of the Operations Research Society of Japan 2007, Vol. 50, No. 4, 325-338 ENUMERATING SPANNING AND CONNECTED SUBSETS IN GRAPHS AND MATROIDS y Leonid Khachiyan Endre Boros Konrad Borys Khaled Elbassioni

More information

Finding All Minimal Infrequent Multi-dimensional Intervals

Finding All Minimal Infrequent Multi-dimensional Intervals Finding All Minimal nfrequent Multi-dimensional ntervals Khaled M. Elbassioni Max-Planck-nstitut für nformatik, Saarbrücken, Germany; elbassio@mpi-sb.mpg.de Abstract. Let D be a database of transactions

More information

R u t c o r Research R e p o r t. Uniform partitions and Erdös-Ko-Rado Theorem a. Vladimir Gurvich b. RRR , August, 2009

R u t c o r Research R e p o r t. Uniform partitions and Erdös-Ko-Rado Theorem a. Vladimir Gurvich b. RRR , August, 2009 R u t c o r Research R e p o r t Uniform partitions and Erdös-Ko-Rado Theorem a Vladimir Gurvich b RRR 16-2009, August, 2009 RUTCOR Rutgers Center for Operations Research Rutgers University 640 Bartholomew

More information

On the Readability of Monotone Boolean Formulae

On the Readability of Monotone Boolean Formulae On the Readability of Monotone Boolean Formulae Khaled Elbassioni 1, Kazuhisa Makino 2, Imran Rauf 1 1 Max-Planck-Institut für Informatik, Saarbrücken, Germany {elbassio,irauf}@mpi-inf.mpg.de 2 Department

More information

Discrete Mathematics

Discrete Mathematics Discrete Mathematics 309 (2009) 4456 4468 Contents lists available at ScienceDirect Discrete Mathematics journal homepage: www.elsevier.com/locate/disc Minimal and locally minimal games and game forms

More information

On the Generation of Circuits and Minimal Forbidden Sets

On the Generation of Circuits and Minimal Forbidden Sets Mathematical Programming manuscript No. (will be inserted by the editor) Frederik Stork Marc Uetz On the Generation of Circuits and Minimal Forbidden Sets January 31, 2004 Abstract We present several complexity

More information

On Resolution Like Proofs of Monotone Self-Dual Functions

On Resolution Like Proofs of Monotone Self-Dual Functions On Resolution Like Proofs of Monotone Self-Dual Functions Daya Ram Gaur Department of Mathematics and Computer Science University of Lethbridge, Lethbridge, AB, Canada, T1K 3M4 gaur@cs.uleth.ca Abstract

More information

Algorithms for Enumerating Circuits in Matroids

Algorithms for Enumerating Circuits in Matroids Algorithms for Enumerating Circuits in Matroids Endre Boros 1, Khaled Elbassioni 1, Vladimir Gurvich 1, and Leonid Khachiyan 2 1 RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway NJ 08854-8003;

More information

On the Complexity of Enumerating Pseudo-intents

On the Complexity of Enumerating Pseudo-intents On the Complexity of Enumerating Pseudo-intents Felix Distel a, Barış Sertkaya,b a Theoretical Computer Science, TU Dresden Nöthnitzer Str. 46 01187 Dresden, Germany b SAP Research Center Dresden Chemtnitzer

More information

Generating k-vertex Connected Spanning Subgraphs and k-edge Connected Spanning Subgraphs

Generating k-vertex Connected Spanning Subgraphs and k-edge Connected Spanning Subgraphs Generating k-vertex Connected Spanning Subgraphs and k-edge Connected Spanning Subgraphs Endre Boros Konrad Borys Khaled Elbassioni Vladimir Gurvich Kazuhisa Makino Gabor Rudolf Abstract We show that k-vertex

More information

Complexity of DNF and Isomorphism of Monotone Formulas

Complexity of DNF and Isomorphism of Monotone Formulas Complexity of DNF and Isomorphism of Monotone Formulas Judy Goldsmith 1, Matthias Hagen 2, Martin Mundhenk 2 1 University of Kentucky, Dept. of Computer Science, Lexington, KY 40506 0046, goldsmit@cs.uky.edu

More information

Translating between Horn Representations. and their Characteristic Models. Abstract

Translating between Horn Representations. and their Characteristic Models. Abstract Journal of Articial Intelligence Research 3 (1995) 349-372 Submitted 5/95; published 12/95 Translating between Horn Representations and their Characteristic Models Roni Khardon Aiken Computation Lab.,

More information

Berge Multiplication for Monotone Boolean Dualization

Berge Multiplication for Monotone Boolean Dualization DIMACS Technical Report 2007-15 Berge Multiplication for Monotone Boolean Dualization 1 by Endre Boros,2 Khaled Elbassioni 3 Kazuhisa Makino 4 1 The first author is thankful for the partial support by

More information

Towards the Complexity of Recognizing Pseudo-intents

Towards the Complexity of Recognizing Pseudo-intents Towards the Complexity of Recognizing Pseudo-intents Barış Sertkaya Theoretical Computer Science TU Dresden, Germany July 30, 2009 Supported by the German Research Foundation (DFG) under grant BA 1122/12-1

More information

Project in Computational Game Theory: Communities in Social Networks

Project in Computational Game Theory: Communities in Social Networks Project in Computational Game Theory: Communities in Social Networks Eldad Rubinstein November 11, 2012 1 Presentation of the Original Paper 1.1 Introduction In this section I present the article [1].

More information

An Efficient Implementation of a Quasi-polynomial Algorithm for Generating Hypergraph Transversals

An Efficient Implementation of a Quasi-polynomial Algorithm for Generating Hypergraph Transversals An Efficient Implementation of a Quasi-polynomial Algorithm for Generating Hypergraph Transversals E. Boros 1, K. Elbassioni 1, V. Gurvich 1, and Leonid Khachiyan 2 1 RUTCOR, Rutgers University, 640 Bartholomew

More information

On the Readability of Monotone Boolean Formulae

On the Readability of Monotone Boolean Formulae On the Readability of Monotone Boolean Formulae Khaled Elbassioni Kazuhisa Maino Imran Rauf January 30, 2009 Abstract Golumbic et al. [Discrete Applied Mathematics 1542006 1465-1477] defined the readability

More information

R u t c o r Research R e p o r t. Minimal and locally minimal games and game forms. 1. Endre Boros 2 Vladimir Gurvich 3 Kazuhisa Makino 4

R u t c o r Research R e p o r t. Minimal and locally minimal games and game forms. 1. Endre Boros 2 Vladimir Gurvich 3 Kazuhisa Makino 4 R u t c o r Research R e p o r t Minimal and locally minimal games and game forms. 1 Endre Boros 2 Vladimir Gurvich 3 Kazuhisa Makino 4 RRR 28-2008, October 2008 RUTCOR Rutgers Center for Operations Research

More information

Acyclic, or totally tight, two-person game forms; characterization and main properties 1

Acyclic, or totally tight, two-person game forms; characterization and main properties 1 DIMACS Technical Report 008-0 October 008 Acyclic, or totally tight, two-person game forms; characterization and main properties by Endre Boros RUTCOR, Rutgers University 640 Bartholomew Road Piscataway,

More information

An Introduction to Proof Complexity, Part II.

An Introduction to Proof Complexity, Part II. An Introduction to Proof Complexity, Part II. Pavel Pudlák Mathematical Institute, Academy of Sciences, Prague and Charles University, Prague Computability in Europe 2009, Heidelberg 2 Overview Part I.

More information

Separation, Inverse Optimization, and Decomposition. Some Observations. Ted Ralphs 1 Joint work with: Aykut Bulut 1

Separation, Inverse Optimization, and Decomposition. Some Observations. Ted Ralphs 1 Joint work with: Aykut Bulut 1 : Some Observations Ted Ralphs 1 Joint work with: Aykut Bulut 1 1 COR@L Lab, Department of Industrial and Systems Engineering, Lehigh University MOA 2016, Beijing, China, 27 June 2016 What Is This Talk

More information

Computational Completeness

Computational Completeness Computational Completeness 1 Definitions and examples Let Σ = {f 1, f 2,..., f i,...} be a (finite or infinite) set of Boolean functions. Any of the functions f i Σ can be a function of arbitrary number

More information

The subject of this paper is nding small sample spaces for joint distributions of

The subject of this paper is nding small sample spaces for joint distributions of Constructing Small Sample Spaces for De-Randomization of Algorithms Daphne Koller Nimrod Megiddo y September 1993 The subject of this paper is nding small sample spaces for joint distributions of n Bernoulli

More information

Lecture #21. c T x Ax b. maximize subject to

Lecture #21. c T x Ax b. maximize subject to COMPSCI 330: Design and Analysis of Algorithms 11/11/2014 Lecture #21 Lecturer: Debmalya Panigrahi Scribe: Samuel Haney 1 Overview In this lecture, we discuss linear programming. We first show that the

More information

CONSEQUENCES OF POWER SERIES REPRESENTATION

CONSEQUENCES OF POWER SERIES REPRESENTATION CONSEQUENCES OF POWER SERIES REPRESENTATION 1. The Uniqueness Theorem Theorem 1.1 (Uniqueness). Let Ω C be a region, and consider two analytic functions f, g : Ω C. Suppose that S is a subset of Ω that

More information

CS Foundations of Communication Complexity

CS Foundations of Communication Complexity CS 49 - Foundations of Communication Complexity Lecturer: Toniann Pitassi 1 The Discrepancy Method Cont d In the previous lecture we ve outlined the discrepancy method, which is a method for getting lower

More information

Computing Solution Concepts of Normal-Form Games. Song Chong EE, KAIST

Computing Solution Concepts of Normal-Form Games. Song Chong EE, KAIST Computing Solution Concepts of Normal-Form Games Song Chong EE, KAIST songchong@kaist.edu Computing Nash Equilibria of Two-Player, Zero-Sum Games Can be expressed as a linear program (LP), which means

More information

11.1 Set Cover ILP formulation of set cover Deterministic rounding

11.1 Set Cover ILP formulation of set cover Deterministic rounding CS787: Advanced Algorithms Lecture 11: Randomized Rounding, Concentration Bounds In this lecture we will see some more examples of approximation algorithms based on LP relaxations. This time we will use

More information

Expander Construction in VNC 1

Expander Construction in VNC 1 Expander Construction in VNC 1 Sam Buss joint work with Valentine Kabanets, Antonina Kolokolova & Michal Koucký Prague Workshop on Bounded Arithmetic November 2-3, 2017 Talk outline I. Combinatorial construction

More information

Approximating the volume of unions and intersections of high-dimensional geometric objects

Approximating the volume of unions and intersections of high-dimensional geometric objects Approximating the volume of unions and intersections of high-dimensional geometric objects Karl Bringmann 1 and Tobias Friedrich 2,3 1 Universität des Saarlandes, Saarbrücken, Germany 2 Max-Planck-Institut

More information

Endre Boros a Vladimir Gurvich c ; b Max-Planck-Institut für Informatik, Saarbrücken, Germany;

Endre Boros a Vladimir Gurvich c ; b Max-Planck-Institut für Informatik, Saarbrücken, Germany; R u t c o r Research R e p o r t Generating vertices of polyhedra and related problems of monotone generation Endre Boros a Vladimir Gurvich c Khaled Elbassioni b Kazuhisa Makino d RRR 12-2007, March 2007

More information

Single Machine Scheduling with Generalized Total Tardiness Objective Function

Single Machine Scheduling with Generalized Total Tardiness Objective Function Single Machine Scheduling with Generalized Total Tardiness Objective Function Evgeny R. Gafarov a, Alexander A. Lazarev b Institute of Control Sciences of the Russian Academy of Sciences, Profsoyuznaya

More information

Generating vertices of polyhedra and related monotone generation problems

Generating vertices of polyhedra and related monotone generation problems DIMACS Technical Report 2007-03 March 2007 Generating vertices of polyhedra and related monotone generation problems by Endre Boros RUTCOR, Rutgers University 640 Bartholomew Road Piscataway, NJ 08854-8003

More information

An approximation algorithm for the minimum latency set cover problem

An approximation algorithm for the minimum latency set cover problem An approximation algorithm for the minimum latency set cover problem Refael Hassin 1 and Asaf Levin 2 1 Department of Statistics and Operations Research, Tel-Aviv University, Tel-Aviv, Israel. hassin@post.tau.ac.il

More information

Santa Claus Schedules Jobs on Unrelated Machines

Santa Claus Schedules Jobs on Unrelated Machines Santa Claus Schedules Jobs on Unrelated Machines Ola Svensson (osven@kth.se) Royal Institute of Technology - KTH Stockholm, Sweden March 22, 2011 arxiv:1011.1168v2 [cs.ds] 21 Mar 2011 Abstract One of the

More information

Addition is exponentially harder than counting for shallow monotone circuits

Addition is exponentially harder than counting for shallow monotone circuits Addition is exponentially harder than counting for shallow monotone circuits Igor Carboni Oliveira Columbia University / Charles University in Prague Joint work with Xi Chen (Columbia) and Rocco Servedio

More information

Some Sieving Algorithms for Lattice Problems

Some Sieving Algorithms for Lattice Problems Foundations of Software Technology and Theoretical Computer Science (Bangalore) 2008. Editors: R. Hariharan, M. Mukund, V. Vinay; pp - Some Sieving Algorithms for Lattice Problems V. Arvind and Pushkar

More information

Lecture 20: conp and Friends, Oracles in Complexity Theory

Lecture 20: conp and Friends, Oracles in Complexity Theory 6.045 Lecture 20: conp and Friends, Oracles in Complexity Theory 1 Definition: conp = { L L NP } What does a conp computation look like? In NP algorithms, we can use a guess instruction in pseudocode:

More information

Lecture Note: Rounding From pseudo-polynomial to FPTAS

Lecture Note: Rounding From pseudo-polynomial to FPTAS Lecture Note: Rounding From pseudo-polynomial to FPTAS for Approximation Algorithms course in CCU Bang Ye Wu CSIE, Chung Cheng University, Taiwan Rounding Rounding is a kind of techniques used to design

More information

MATHEMATICAL ENGINEERING TECHNICAL REPORTS. On the Boolean Connectivity Problem for Horn Relations

MATHEMATICAL ENGINEERING TECHNICAL REPORTS. On the Boolean Connectivity Problem for Horn Relations MATHEMATICAL ENGINEERING TECHNICAL REPORTS On the Boolean Connectivity Problem for Horn Relations Kazuhisa Makino, Suguru Tamaki, and Masaki Yamamoto METR 2007 25 April 2007 DEPARTMENT OF MATHEMATICAL

More information

Model Counting for Logical Theories

Model Counting for Logical Theories Model Counting for Logical Theories Wednesday Dmitry Chistikov Rayna Dimitrova Department of Computer Science University of Oxford, UK Max Planck Institute for Software Systems (MPI-SWS) Kaiserslautern

More information

2 Notation and Preliminaries

2 Notation and Preliminaries On Asymmetric TSP: Transformation to Symmetric TSP and Performance Bound Ratnesh Kumar Haomin Li epartment of Electrical Engineering University of Kentucky Lexington, KY 40506-0046 Abstract We show that

More information

Bounded Arithmetic, Expanders, and Monotone Propositional Proofs

Bounded Arithmetic, Expanders, and Monotone Propositional Proofs Bounded Arithmetic, Expanders, and Monotone Propositional Proofs joint work with Valentine Kabanets, Antonina Kolokolova & Michal Koucký Takeuti Symposium on Advances in Logic Kobe, Japan September 20,

More information

Exam in Discrete Mathematics

Exam in Discrete Mathematics Exam in Discrete Mathematics First Year at the Faculty of Engineering and Science and the Technical Faculty of IT and Design June 4th, 018, 9.00-1.00 This exam consists of 11 numbered pages with 14 problems.

More information

COT 6936: Topics in Algorithms! Giri Narasimhan. ECS 254A / EC 2443; Phone: x3748

COT 6936: Topics in Algorithms! Giri Narasimhan. ECS 254A / EC 2443; Phone: x3748 COT 6936: Topics in Algorithms! Giri Narasimhan ECS 254A / EC 2443; Phone: x3748 giri@cs.fiu.edu https://moodle.cis.fiu.edu/v2.1/course/view.php?id=612 Gaussian Elimination! Solving a system of simultaneous

More information

The random variable 1

The random variable 1 The random variable 1 Contents 1. Definition 2. Distribution and density function 3. Specific random variables 4. Functions of one random variable 5. Mean and variance 2 The random variable A random variable

More information

MATHEMATICAL ENGINEERING TECHNICAL REPORTS. Deductive Inference for the Interiors and Exteriors of Horn Theories

MATHEMATICAL ENGINEERING TECHNICAL REPORTS. Deductive Inference for the Interiors and Exteriors of Horn Theories MATHEMATICAL ENGINEERING TECHNICAL REPORTS Deductive Inference for the Interiors and Exteriors of Horn Theories Kazuhisa MAKINO and Hirotaka ONO METR 2007 06 February 2007 DEPARTMENT OF MATHEMATICAL INFORMATICS

More information

On Allocating Goods to Maximize Fairness

On Allocating Goods to Maximize Fairness 2009 50th Annual IEEE Symposium on Foundations of Computer Science On Allocating Goods to Maximize Fairness Deeparnab Chakrabarty Dept. of C&O University of Waterloo Waterloo, ON N2L 3G1 Email: deepc@math.uwaterloo.ca

More information

CS 6820 Fall 2014 Lectures, October 3-20, 2014

CS 6820 Fall 2014 Lectures, October 3-20, 2014 Analysis of Algorithms Linear Programming Notes CS 6820 Fall 2014 Lectures, October 3-20, 2014 1 Linear programming The linear programming (LP) problem is the following optimization problem. We are given

More information

1 Perfect Matching and Matching Polytopes

1 Perfect Matching and Matching Polytopes CS 598CSC: Combinatorial Optimization Lecture date: /16/009 Instructor: Chandra Chekuri Scribe: Vivek Srikumar 1 Perfect Matching and Matching Polytopes Let G = (V, E be a graph. For a set E E, let χ E

More information

Reducibility in polynomialtime computable analysis. Akitoshi Kawamura (U Tokyo) Wadern, Germany, September 24, 2015

Reducibility in polynomialtime computable analysis. Akitoshi Kawamura (U Tokyo) Wadern, Germany, September 24, 2015 Reducibility in polynomialtime computable analysis Akitoshi Kawamura (U Tokyo) Wadern, Germany, September 24, 2015 Computable Analysis Applying computability / complexity theory to problems involving real

More information

Outline. Complexity Theory. Example. Sketch of a log-space TM for palindromes. Log-space computations. Example VU , SS 2018

Outline. Complexity Theory. Example. Sketch of a log-space TM for palindromes. Log-space computations. Example VU , SS 2018 Complexity Theory Complexity Theory Outline Complexity Theory VU 181.142, SS 2018 3. Logarithmic Space Reinhard Pichler Institute of Logic and Computation DBAI Group TU Wien 3. Logarithmic Space 3.1 Computational

More information

4. How to prove a problem is NPC

4. How to prove a problem is NPC The reducibility relation T is transitive, i.e, A T B and B T C imply A T C Therefore, to prove that a problem A is NPC: (1) show that A NP (2) choose some known NPC problem B define a polynomial transformation

More information

Minimization of Symmetric Submodular Functions under Hereditary Constraints

Minimization of Symmetric Submodular Functions under Hereditary Constraints Minimization of Symmetric Submodular Functions under Hereditary Constraints J.A. Soto (joint work with M. Goemans) DIM, Univ. de Chile April 4th, 2012 1 of 21 Outline Background Minimal Minimizers and

More information

Introduction to Statistical Learning Theory

Introduction to Statistical Learning Theory Introduction to Statistical Learning Theory Definition Reminder: We are given m samples {(x i, y i )} m i=1 Dm and a hypothesis space H and we wish to return h H minimizing L D (h) = E[l(h(x), y)]. Problem

More information

Lecture 18: PCP Theorem and Hardness of Approximation I

Lecture 18: PCP Theorem and Hardness of Approximation I Lecture 18: and Hardness of Approximation I Arijit Bishnu 26.04.2010 Outline 1 Introduction to Approximation Algorithm 2 Outline 1 Introduction to Approximation Algorithm 2 Approximation Algorithm Approximation

More information

arxiv: v1 [cs.ds] 3 May 2018

arxiv: v1 [cs.ds] 3 May 2018 On the Enumeration of Minimal Hitting Sets in Lexicographical Order arxiv:1805.01310v1 [cs.ds] 3 May 2018 Thomas Bläsius Hasso Plattner Institute Potsdam, Germany Tobias Friedrich Hasso Plattner Institute

More information

Approximation Algorithms for the k-set Packing Problem

Approximation Algorithms for the k-set Packing Problem Approximation Algorithms for the k-set Packing Problem Marek Cygan Institute of Informatics University of Warsaw 20th October 2016, Warszawa Marek Cygan Approximation Algorithms for the k-set Packing Problem

More information

Nash-solvable bidirected cyclic two-person game forms

Nash-solvable bidirected cyclic two-person game forms DIMACS Technical Report 2008-13 November 2008 Nash-solvable bidirected cyclic two-person game forms by Endre Boros 1 RUTCOR, Rutgers University 640 Bartholomew Road, Piscataway NJ 08854-8003 boros@rutcor.rutgers.edu

More information

MINIMIZING TOTAL TARDINESS FOR SINGLE MACHINE SEQUENCING

MINIMIZING TOTAL TARDINESS FOR SINGLE MACHINE SEQUENCING Journal of the Operations Research Society of Japan Vol. 39, No. 3, September 1996 1996 The Operations Research Society of Japan MINIMIZING TOTAL TARDINESS FOR SINGLE MACHINE SEQUENCING Tsung-Chyan Lai

More information

The Complexity of Maximum. Matroid-Greedoid Intersection and. Weighted Greedoid Maximization

The Complexity of Maximum. Matroid-Greedoid Intersection and. Weighted Greedoid Maximization Department of Computer Science Series of Publications C Report C-2004-2 The Complexity of Maximum Matroid-Greedoid Intersection and Weighted Greedoid Maximization Taneli Mielikäinen Esko Ukkonen University

More information

Dominating Set. Chapter 7

Dominating Set. Chapter 7 Chapter 7 Dominating Set In this chapter we present another randomized algorithm that demonstrates the power of randomization to break symmetries. We study the problem of finding a small dominating set

More information

Hybrid Machine Learning Algorithms

Hybrid Machine Learning Algorithms Hybrid Machine Learning Algorithms Umar Syed Princeton University Includes joint work with: Rob Schapire (Princeton) Nina Mishra, Alex Slivkins (Microsoft) Common Approaches to Machine Learning!! Supervised

More information

CSE 312 Final Review: Section AA

CSE 312 Final Review: Section AA CSE 312 TAs December 8, 2011 General Information General Information Comprehensive Midterm General Information Comprehensive Midterm Heavily weighted toward material after the midterm Pre-Midterm Material

More information

1 Continuous extensions of submodular functions

1 Continuous extensions of submodular functions CS 369P: Polyhedral techniques in combinatorial optimization Instructor: Jan Vondrák Lecture date: November 18, 010 1 Continuous extensions of submodular functions Submodular functions are functions assigning

More information

Endre Boros b Vladimir Gurvich d ;

Endre Boros b Vladimir Gurvich d ; R u t c o r Research R e p o r t On effectivity functions of game forms a Endre Boros b Vladimir Gurvich d Khaled Elbassioni c Kazuhisa Makino e RRR 03-2009, February 2009 RUTCOR Rutgers Center for Operations

More information

Splitting the Computational Universe

Splitting the Computational Universe Splitting the Computational Universe s Theorem Definition Existential Second Order Logic (ESO) is the collection of all statements of the form: ( S )Φ(S, S ) s Theorem Definition Existential Second Order

More information

6.254 : Game Theory with Engineering Applications Lecture 8: Supermodular and Potential Games

6.254 : Game Theory with Engineering Applications Lecture 8: Supermodular and Potential Games 6.254 : Game Theory with Engineering Applications Lecture 8: Supermodular and Asu Ozdaglar MIT March 2, 2010 1 Introduction Outline Review of Supermodular Games Reading: Fudenberg and Tirole, Section 12.3.

More information

Proofs with monotone cuts

Proofs with monotone cuts Proofs with monotone cuts Emil Jeřábek jerabek@math.cas.cz http://math.cas.cz/ jerabek/ Institute of Mathematics of the Academy of Sciences, Prague Logic Colloquium 2010, Paris Propositional proof complexity

More information

Separation, Inverse Optimization, and Decomposition. Some Observations. Ted Ralphs 1 Joint work with: Aykut Bulut 1

Separation, Inverse Optimization, and Decomposition. Some Observations. Ted Ralphs 1 Joint work with: Aykut Bulut 1 : Some Observations Ted Ralphs 1 Joint work with: Aykut Bulut 1 1 COR@L Lab, Department of Industrial and Systems Engineering, Lehigh University COLGEN 2016, Buzios, Brazil, 25 May 2016 What Is This Talk

More information

The Parameterized Complexity of Approximate Inference in Bayesian Networks

The Parameterized Complexity of Approximate Inference in Bayesian Networks The Parameterized Complexity of Approximate Inference in Bayesian Networks Johan Kwisthout j.kwisthout@donders.ru.nl http://www.dcc.ru.nl/johank/ Donders Center for Cognition Department of Artificial Intelligence

More information

3.1 Asymptotic notation

3.1 Asymptotic notation 3.1 Asymptotic notation The notations we use to describe the asymptotic running time of an algorithm are defined in terms of functions whose domains are the set of natural numbers N = {0, 1, 2,... Such

More information

Finding Satisfying Assignments by Random Walk

Finding Satisfying Assignments by Random Walk Ferienakademie, Sarntal 2010 Finding Satisfying Assignments by Random Walk Rolf Wanka, Erlangen Overview Preliminaries A Randomized Polynomial-time Algorithm for 2-SAT A Randomized O(2 n )-time Algorithm

More information

with the size of the input in the limit, as the size of the misused.

with the size of the input in the limit, as the size of the misused. Chapter 3. Growth of Functions Outline Study the asymptotic efficiency of algorithms Give several standard methods for simplifying the asymptotic analysis of algorithms Present several notational conventions

More information

Solution of the 8 th Homework

Solution of the 8 th Homework Solution of the 8 th Homework Sangchul Lee December 8, 2014 1 Preinary 1.1 A simple remark on continuity The following is a very simple and trivial observation. But still this saves a lot of words in actual

More information

Approximation Schemes for Scheduling on Parallel Machines

Approximation Schemes for Scheduling on Parallel Machines Approximation Schemes for Scheduling on Parallel Machines Noga Alon Yossi Azar Gerhard J. Woeginger Tal Yadid Abstract We discuss scheduling problems with m identical machines and n jobs where each job

More information

Computational Complexity of Bayesian Networks

Computational Complexity of Bayesian Networks Computational Complexity of Bayesian Networks UAI, 2015 Complexity theory Many computations on Bayesian networks are NP-hard Meaning (no more, no less) that we cannot hope for poly time algorithms that

More information

On Maximizing Welfare when Utility Functions are Subadditive

On Maximizing Welfare when Utility Functions are Subadditive On Maximizing Welfare when Utility Functions are Subadditive Uriel Feige October 8, 2007 Abstract We consider the problem of maximizing welfare when allocating m items to n players with subadditive utility

More information

Dominating Set. Chapter 26

Dominating Set. Chapter 26 Chapter 26 Dominating Set In this chapter we present another randomized algorithm that demonstrates the power of randomization to break symmetries. We study the problem of finding a small dominating set

More information

Dominating Set. Chapter Sequential Greedy Algorithm 294 CHAPTER 26. DOMINATING SET

Dominating Set. Chapter Sequential Greedy Algorithm 294 CHAPTER 26. DOMINATING SET 294 CHAPTER 26. DOMINATING SET 26.1 Sequential Greedy Algorithm Chapter 26 Dominating Set Intuitively, to end up with a small dominating set S, nodes in S need to cover as many neighbors as possible. It

More information

One-parameter families of functions in the Pick class

One-parameter families of functions in the Pick class arxiv:0704.1716v1 [math.ds] 13 Apr 2007 One-parameter families of functions in the Pick class Waldemar Pa luba Institute of Mathematics Warsaw University Banacha 2 02-097 Warsaw, Poland e-mail: paluba@mimuw.edu.pl

More information

Improved Algorithms for Machine Allocation in Manufacturing Systems

Improved Algorithms for Machine Allocation in Manufacturing Systems Improved Algorithms for Machine Allocation in Manufacturing Systems Hans Frenk Martine Labbé Mario van Vliet Shuzhong Zhang October, 1992 Econometric Institute, Erasmus University Rotterdam, the Netherlands.

More information

Approximation Schemes for Scheduling on Uniformly Related and Identical Parallel Machines

Approximation Schemes for Scheduling on Uniformly Related and Identical Parallel Machines Approximation Schemes for Scheduling on Uniformly Related and Identical Parallel Machines Leah Epstein Jiří Sgall Abstract We give a polynomial approximation scheme for the problem of scheduling on uniformly

More information

Randomness and Computation

Randomness and Computation Randomness and Computation or, Randomized Algorithms Mary Cryan School of Informatics University of Edinburgh RC (2018/19) Lecture 11 slide 1 The Probabilistic Method The Probabilistic Method is a nonconstructive

More information