Bilevel optimization is a very active field of applied mathematics. The main reason is that
bilevel optimization problems can serve as a powerful tool for modeling hierarchical …

We consider the\sf Unconstrained Submodular Maximization problem in which we are given
a nonnegative submodular function f:2^N→R^+, and the objective is to find a subset S⊆N …

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 …

We improve the effectiveness of propagation-and linear-optimization-based neural network
verification algorithms with a new tightened convex relaxation for ReLU neurons. Unlike …

We consider a family of competitive facility location problems in which a “newcomer”
company enters the market and has to decide where to locate a set of new facilities so as to …

Motivated by online decision-making in time-varying combinatorial environments, we study
the problem of transforming offline algorithms to their online counterparts. We focus on …

We propose a scenario decomposition algorithm for stochastic 0–1 programs. The algorithm
recovers an optimal solution by iteratively exploring and cutting-off candidate solutions …

We consider stochastic influence maximization problems arising in social networks. In
contrast to existing studies that involve greedy approximation algorithms with a 63 …

We study the problem of maximizing a stochastic monotone submodular function with
respect to a matroid constraint. Because of the presence of diminishing marginal values in …

We describe strong convex valid inequalities for conic quadratic mixed 0–1 optimization.
These inequalities can be utilized for solving numerous practical nonlinear discrete …