Efficient Minimization of Decomposable Submodular Functions

Many combinatorial problems arising in machine learning can be reduced to the problem of minimizing a submodular function. Submodular functions are a natural discrete analog of convex functions, and can be minimized in strongly polynomial time. Unfortunately, state-of-the-art algorithms for general submodular minimization are intractable for larger problems. In this paper, we introduce a novel subclass of submodular minimization problems that we call decomposable. Decomposable submodular functions are those that can be represented as sums of concave functions applied to modular functions. We develop an algorithm, SLG, that can efficiently minimize decomposable submodular functions with tens of thousands of variables. Our algorithm exploits recent results in smoothed convex minimization. We apply SLG to synthetic benchmarks and a joint classification-and-segmentation task, and show that it outperforms the state-of-the-art general purpose submodular minimization algorithms by several orders of magnitude.Comment: Expanded version of paper for Neural Information Processing Systems 201

Comparing Markov Chains: Aggregation and Precedence Relations Applied to Sets of States, with Applications to Assemble-to-Order Systems

International audienceSolving Markov chains is, in general, difficult if the state space of the chain is very large (or infinite) and lacking a simple repeating structure. One alternative to solving such chains is to construct models that are simple to analyze and provide bounds for a reward function of interest. We present a new bounding method for Markov chains inspired by Markov reward theory: Our method constructs bounds by redirecting selected sets of transitions, facilitating an intuitive interpretation of the modifications of the original system. We show that our method is compatible with strong aggregation of Markov chains; thus we can obtain bounds for an initial chain by analyzing a much smaller chain. We illustrate our method by using it to prove monotonicity results and bounds for assemble-to-order systems

Exploiting Local Optimality and Strong Inequalities for Solving Bilevel Combinatorial and Submodular Optimization Problems

Bilevel combinatorial and submodular optimization problems arise in a broad range of real-life applications including price setting, network design, information gathering, viral marketing, and so on. However, the current state-of-the-art solution approaches still have difficulties to solve them exactly for many broad classes of practically relevant problems. In this dissertation, using the concepts of local optimality and strong valid inequalities, we explore the fundamental mathematical structure of these problems and boost the computational performance of exact solution methods for these two important classes of optimization problems. In our initial study, we focus on a class of bilevel spanning tree (BST) problems, motivated by a hierarchical (namely, bilevel) generalization of the classical minimum spanning tree problem. We show that depending on the type of the objective function involved at each level, BST can be solved to optimality either in polynomial time by a specialized algorithm or via a mixed-integer linear programming (MILP) model solvable by an off-the-shelf solver. The latter case corresponds to an NP-hard class of the problem. Our second study proposes a hierarchy of upper and lower bounds for the bilevel problems, where the follower’s variables are all binary. In particular, we develop a generalized bilevel framework that explores the local optimality conditions at the lower level. Submodularity and disjunctive-based approach are then exploited to derive strong MILP formulations for the resulting framework. Computational experiments indicate that the quality of our newly proposed bounds is superior to the current standard approach. Furthermore, we generalize our aforementioned results for BST and show that the proposed bounds are sharp for bilevel matroid problems. Finally, to address the computational challenges in the submodular maximization problem, we present the polyhedral study of its mixed 0–1 set. Specifically, we strengthen some existing results in the literature by finding two families of facet-defining inequalities through the lens of sequence independent lifting. We further extend the scope of this work and describe the multi-dimensional sequence independent lifting for a more complex set. The developed polyhedral results complement the classical results from the literature for the mixed0–1 knapsack and single-node flow sets

Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic