Submodular functions are powerful tools to model and solve either to optimality or approximately many operational research problems including problems defined on graphs. After reviewing some long-standing theoretical results about the structure of local and global maxima of submodular functions, Cherenin's selection rules and his Dichotomy Algorithm, we revise the above mentioned theory and show that our revision is useful for creating new non-binary branching algorithms and finding either approximation solutions with guaranteed accuracy or exact ones. (C) 2008 Elsevier B.V. All rights reserved.</p
Submodular functions occur in many combinatorial optimisation problems and a number of polynomial-ti...
This paper presents the first combinatorial polynomial-time algorithm for minimizing submodular func...
Thesis (Ph.D.)--University of Washington, 2015In this dissertation, we explore a class of unifying a...
Submodular functions are powerful tools to model and solve either to optimality or approximately man...
Submodular functions are powerful tools to model and solve either to optimality or approximately man...
The first purpose of this paper is to make an old (Russian) theoretical results about the structure ...
The first purpose of this paper is to make an old (Russian) theoretical results about the structure ...
Submodularity is a fundamental phenomenon in combinatorial optimization. Submodular func-tions occur...
Submodular functions often arise in various fields of operations research including discrete optimiz...
A litany of questions from a wide variety of scientific disciplines can be cast as non-monotone subm...
Presented at the Georgia Tech Algorithms & Randomness Center workshop: Modern Aspects of Submodular...
Abstract. Submodular functions are discrete functions that model laws of diminishing returns and enj...
Abstract: "Many set functions F in combinatorial optimization satisfy the diminishing returns proper...
We present a practical and powerful new framework for both unconstrained and constrained submodular ...
Submodular functions are the functions that frequently appear in connection with many combi-natorial...
Submodular functions occur in many combinatorial optimisation problems and a number of polynomial-ti...
This paper presents the first combinatorial polynomial-time algorithm for minimizing submodular func...
Thesis (Ph.D.)--University of Washington, 2015In this dissertation, we explore a class of unifying a...
Submodular functions are powerful tools to model and solve either to optimality or approximately man...
Submodular functions are powerful tools to model and solve either to optimality or approximately man...
The first purpose of this paper is to make an old (Russian) theoretical results about the structure ...
The first purpose of this paper is to make an old (Russian) theoretical results about the structure ...
Submodularity is a fundamental phenomenon in combinatorial optimization. Submodular func-tions occur...
Submodular functions often arise in various fields of operations research including discrete optimiz...
A litany of questions from a wide variety of scientific disciplines can be cast as non-monotone subm...
Presented at the Georgia Tech Algorithms & Randomness Center workshop: Modern Aspects of Submodular...
Abstract. Submodular functions are discrete functions that model laws of diminishing returns and enj...
Abstract: "Many set functions F in combinatorial optimization satisfy the diminishing returns proper...
We present a practical and powerful new framework for both unconstrained and constrained submodular ...
Submodular functions are the functions that frequently appear in connection with many combi-natorial...
Submodular functions occur in many combinatorial optimisation problems and a number of polynomial-ti...
This paper presents the first combinatorial polynomial-time algorithm for minimizing submodular func...
Thesis (Ph.D.)--University of Washington, 2015In this dissertation, we explore a class of unifying a...