5 edition of **Submodular Functions and Optimization, Volume 58** found in the catalog.

- 80 Want to read
- 29 Currently reading

Published
**September 15, 2005**
by Elsevier Science
.

Written in English

**Edition Notes**

Second Edition

The Physical Object | |
---|---|

Number of Pages | 410 |

ID Numbers | |

Open Library | OL7531035M |

ISBN 10 | 0444520864 |

ISBN 10 | 9780444520869 |

Foundations and TrendsR in Machine Learning Vol. 6, No. () – c F. Bach DOI: / Learning with Submodular Functions: A Convex Optimization Perspective. A. Atamtürk and V. Narayanan. The submodular knapsack polytope. Discrete Optimization, Google Scholar; M. Conforti and G. Cornuejols. Submodular set functions, matroids and the greedy algorithm: tight worst-case bounds and some generalizations of the Rado-Edmonds theorem.

Submodular set functions, matroids and the greedy algorithm: tight worst-case bounds and some generalizations of the rado-edmonds theorem. Discrete applied . Abstract. In this paper, we demonstrate how scheduling problems with controllable processing times can be reformulated as maximization linear programming problems over a submodular polyhedron intersected with a by: 1.

Mathematical Engineering, Mathematical Optimization, Combinatorial Optimization Discrete Algorithms Graphs, Networks, and Matroids Submodular Functions Linear Programming and the LP-Newton Method Books. S. Fujishige: "Submodular Functions and Optimization" (North-Holland, ) (2nd ed., Elsevier, ), correction;. In this paper, we provide a randomized $(/e)$-approximation for any monotone submodular function and an arbitrary matroid. The algorithm works in the value oracle model. Our main tools are a variant of the pipage rounding technique of Ageev and Sviridenko [ J. by:

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The present 2nd edition is still a unique book on submodular functions, which is essential to students and researchers interested in combinatorial optimization, discrete mathematics, and discrete algorithms in the fields of mathematics, operations research, computer science, and by: The present 2nd edition is still a unique book on submodular functions, which is essential to students and researchers interested in combinatorial optimization, discrete mathematics, and discrete algorithms in the fields of mathematics, operations research, computer science, and economics.

The present 2nd edition is still a unique book on submodular functions, which is essential to students and researchers interested in combinatorial optimization, discrete mathematics, and discrete Volume 58 book in the fields of mathematics, operations research, computer science, and : $ Submodular Functions and Optimization.

Edited by Satoru Fujishige. Vol Pages () Download full volume. Previous volume. Next volume. Chapter VI - Submodular Function Minimization Pages Download PDF; select article Chapter VII - Discrete Convex Analysis.

Submodular Submodular Functions and Optimization and Optimization: Volume 58 by Satoru Fujishige,available at Book Depository with free delivery : Satoru Fujishige. Satoru Fujishige: Submodular Functions and Optimization, Second Edition (Annals of Discrete Mathematics, Vol. 58) (Elsevier, ) 2nd ed.

1st ed. () Table of Contents. Submodular Functions and Optimization. The importance of submodular functions has been widely recognized in recent years in combinatorial optimization. This is the first book devoted to the exposition of the theory of submodular functions from an.

It has widely been recognized that submodular functions play essential roles in efficiently solvable combinatorial optimization problems. Since the publication of the 1st edition of this book fifteen years ago, submodular functions have been showing further increasing importance in optimization, combinatorics, discrete mathematics, algorithmic computer science, and algorithmic economics, and.

submodular functions often appear as objective functions of machine learning tasks such as sensor placement, document summarization or active learning!simple algorithms such as Greedy or Local Search work well. Jan Vondrák (IBM Almaden) Submodular Functions and Applications 7 / It has widely been recognized that submodular functions play essential roles in efficiently solvable combinatorial optimization problems.

Since the publication of the 1st edition of this book fifteen years ago, submodular functions have been showing further increasing importance in optimization, combinatorics, discrete mathematics, algorithmic computer science, and algorithmic economics, and Author: Satoru Fujishige.

Those works are mainly on submodular function optimization, [32] Fujishige, S.: Submodular Functions and Optimization, vol.

Elsevier Science, Amsterdam () an important role in the. Submodular set function maximization. Unlike the case of minimization, maximizing a submodular functions is NP-hard even in the unconstrained setting.

For instance max cut is a special case even when the function is required only to be non-negative. The unconstrained problem can be shown to be inapproximable if it is allowed to be negative.

The importance of submodular functions has been widely recognized in recent years in combinatorial optimization. This is the first book devoted to the exposition of the theory of submodular functions Book Edition: 1. Search in this book series.

Submodular Functions and Optimization. Edited by Satoru Fujishige. Vol Pages ii-vi, () Download full volume. Previous volume. Next volume.

Chapter V. Nonlinear Optimization with Submodular Constraints Pages. Submodular Analysis, Duality and Optimization Yao-Liang Yu [email protected] Dept. of Computing Science University of Alberta Decem This note is intended to present some fundamental results about submodular functions and their applications in discrete optimization (with special interest in machine learning applications).File Size: KB.

optimization [Sch03] is a great reference both for matroids and submodular functions, and includes Schrijver’s own submodular minimization algorithm.

Finally, anyone looking for an additional level of abstraction from submodu-lar functions should look at Murota’s monograph on discrete convex analysis [Mur03]. This book introduces the notion. Learning with Submodular Functions.

Submodular functions are relevant to machine learning for at least two reasons: (1) some problems may be expressed directly as the optimization of submodular functions, and (2) the Lovász extension of submodular functions provides a useful set of regularization functions for supervised and unsupervised by: Algorithms for Optimizing the Ratio of Submodular Functions Wenruo Bai [email protected] Rishabh Iyer [email protected] Kai Wei @ Jeff Bilmes [email protected] University of Washington, Seattle, WAUSA Abstract We investigate a new optimization problem in-volving minimizing the Ratio of two Submodular (RS) by: 8.

Many combinatorial optimization problems have natural formulations as submodular minimization problems over well-studied combinatorial structures.

A standard approach to these problems is to linearize the objective function by introducing new variables and Cited by: 6. SIAM J. COMPUT. c Society for Industrial and Applied Mathematics Vol.

47, No. 3, pp. { SUBMODULAR FUNCTIONS: LEARNABILITY, STRUCTURE, AND OPTIMIZATION MARIA-FLORINA BALCANyAND NICHOLAS J. HARVEYz Abstract. Submodular functions are discrete functions that model laws of diminishing returnsFile Size: KB. Submodular function maximization is a central problem in combinatorial optimization, generalizing many important problems including Max Cut in directed/undirected graphs and in hypergraphs, certain constraint satisfaction problems, maximum entropy sampling, and maximum facility location by: Optimization of Submodular Functions Tutorial - lecture I Jan Vondrák1 1IBM Almaden Research Center San Jose, CA Jan Vondrák (IBM Almaden) Submodular Optimization Tutorial 1 / 1File Size: KB.Learning with Submodular Functions: A Convex Optimization Perspective FrancisBach1 1 INRIA - SIERRA Project-Team, Laboratoire d’Informatique de l’Ecole Normale Sup´erieure, 23, avenue d’Italie, Paris,France, @ Abstract Submodular functions are relevant to machine learning for mainly two.