Add to ORKGWe consider the application of mixed-integer linear programming (MILP) solvers to the minimization of submodular functions. We evaluate common large-scale linear-programming (LP) techniques (e.g., column generation, row generation, dual stabilization) for solving a LP reformulation of the submodular minimization (SM) problem. We present heuristics based on the LP framework and a MILP solver. We evaluated the performance of our methods on a test bed of min-cut and matroid-intersection problems formulated as SM problems
In this paper, we study the structure of optimal solutions to the submodular function minimization p...
Solving (mixed) integer (linear) programs, (M)I(L)Ps for short, is a fundamental optimisation task w...
It is becoming increasingly evident that many ma-chine learning problems may be reduced to sub-modul...
Various kinds of optimization problems involve nonlinear functions of binary variables that exhibit ...
Thesis (Ph.D.)--University of Washington, 2015In this dissertation, we explore a class of unifying a...
Presented at the Georgia Tech Algorithms & Randomness Center workshop: Modern Aspects of Submodular...
Submodular functions often arise in various fields of operations research including discrete optimiz...
We introduce generalized subadditive generator functions for mixed integer linear programs. Our resu...
Submodular functions occur in many combinatorial optimisation problems and a number of polynomial-ti...
We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a dif...
We present an optimal, combinatorial 1-1/e approximation algorithm for monotone submodular optimizat...
Several key problems in machine learning, such as feature selection and active learning, can be form...
Submodular functions are powerful tools to model and solve either to optimality or approximately man...
Submodular functions are common in combinatorics; examples include the cut capacity function of a gr...
Maximization or minimization problems in which, for each input there is a set of feasible solutions ...
In this paper, we study the structure of optimal solutions to the submodular function minimization p...
Solving (mixed) integer (linear) programs, (M)I(L)Ps for short, is a fundamental optimisation task w...
It is becoming increasingly evident that many ma-chine learning problems may be reduced to sub-modul...
Various kinds of optimization problems involve nonlinear functions of binary variables that exhibit ...
Thesis (Ph.D.)--University of Washington, 2015In this dissertation, we explore a class of unifying a...
Presented at the Georgia Tech Algorithms & Randomness Center workshop: Modern Aspects of Submodular...
Submodular functions often arise in various fields of operations research including discrete optimiz...
We introduce generalized subadditive generator functions for mixed integer linear programs. Our resu...
Submodular functions occur in many combinatorial optimisation problems and a number of polynomial-ti...
We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a dif...
We present an optimal, combinatorial 1-1/e approximation algorithm for monotone submodular optimizat...
Several key problems in machine learning, such as feature selection and active learning, can be form...
Submodular functions are powerful tools to model and solve either to optimality or approximately man...
Submodular functions are common in combinatorics; examples include the cut capacity function of a gr...
Maximization or minimization problems in which, for each input there is a set of feasible solutions ...
In this paper, we study the structure of optimal solutions to the submodular function minimization p...
Solving (mixed) integer (linear) programs, (M)I(L)Ps for short, is a fundamental optimisation task w...
It is becoming increasingly evident that many ma-chine learning problems may be reduced to sub-modul...