Performance bounds for greedy strategies in submodular optimization problems

2018 Summer.Includes bibliographical references.To view the abstract, please see the full text of the document

Non-Smooth, H\"older-Smooth, and Robust Submodular Maximization

We study the problem of maximizing a continuous DR-submodular function that is not necessarily smooth. We prove that the continuous greedy algorithm achieves an [(1-1/e)\OPT-\epsilon] guarantee when the function is monotone and H\"older-smooth, meaning that it admits a H\"older-continuous gradient. For functions that are non-differentiable or non-smooth, we propose a variant of the mirror-prox algorithm that attains an [(1/2)\OPT-\epsilon] guarantee. We apply our algorithmic frameworks to robust submodular maximization and distributionally robust submodular maximization under Wasserstein ambiguity. In particular, the mirror-prox method applies to robust submodular maximization to obtain a single feasible solution whose value is at least (1/2)\OPT-\epsilon. For distributionally robust maximization under Wasserstein ambiguity, we deduce and work over a submodular-convex maximin reformulation whose objective function is H\"older-smooth, for which we may apply both the continuous greedy and the mirror-prox algorithms

Tractability through approximation : a study of two discrete optimization problems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004.Includes bibliographical references.(cont.) algorithm, at one extreme, and complete enumeration, at the other extreme. We derive worst-case approximation guarantees on the solution produced by such an algorithm for matroids. We then define a continuous relaxation of the original problem and show that some of the derived bounds apply with respect to the relaxed problem. We also report on a new bound for independence systems. These bounds extend, and in some cases strengthen, previously known results for standard best-in greedy.This dissertation consists of two parts. In the first part, we address a class of weakly-coupled multi-commodity network design problems characterized by restrictions on path flows and 'soft' demand requirements. In the second part, we address the abstract problem of maximizing non-decreasing submodular functions over independence systems, which arises in a variety of applications such as combinatorial auctions and facility location. Our objective is to develop approximate solution procedures suitable for large-scale instances that provide a continuum of trade-offs between accuracy and tractability. In Part I, we review the application of Dantzig-Wolfe decomposition to mixed-integer programs. We then define a class of multi-commodity network design problems that are weakly-coupled in the flow variables. We show that this problem is NP-complete, and proceed to develop an approximation/reformulation solution approach based on Dantzig-Wolfe decomposition. We apply the ideas developed to the specific problem of airline fleet assignment with the goal of creating models that incorporate more realistic revenue functions. This yields a new formulation of the problem with a provably stronger linear programming relaxation, and we provide some empirical evidence that it performs better than other models proposed in the literature. In Part II, we investigate the performance of a family of greedy-type algorithms to the problem of maximizing submodular functions over independence systems. Building on pioneering work by Conforti, Cornu6jols, Fisher, Jenkyns, Nemhauser, Wolsey and others, we analyze a greedy algorithm that incrementally augments the current solution by adding subsets of arbitrary variable cardinality. This generalizes the standard best-in greedyby Amr Farahat.Ph.D

Conditional Gradient Methods

The purpose of this survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for function minimization. These algorithms are especially useful in convex optimization when linear optimization is cheaper than projections. The selection of the material has been guided by the principle of highlighting crucial ideas as well as presenting new approaches that we believe might become important in the future, with ample citations even of old works imperative in the development of newer methods. Yet, our selection is sometimes biased, and need not reflect consensus of the research community, and we have certainly missed recent important contributions. After all the research area of Frank--Wolfe is very active, making it a moving target. We apologize sincerely in advance for any such distortions and we fully acknowledge: We stand on the shoulder of giants.Comment: 238 pages with many figures. The FrankWolfe.jl Julia package (https://github.com/ZIB-IOL/FrankWolfe.jl) providces state-of-the-art implementations of many Frank--Wolfe method

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum