Bounds for nested law invariant coherent risk measures

With every law invariant coherent risk measure is associated its conditional analogue. In this paper we discuss lower and upper bounds for the corresponding nested (composite) formulations of law invariant coherent risk measures. In particular, we consider the Average Value-at-Risk and comonotonic risk measures. ArticleDownload View PDF

An adaptive accelerated first-order method for convex optimization

This paper presents a new accelerated variant of Nesterov’s method for solving composite convex optimization problems in which certain acceleration parameters are adaptively (and aggressively) chosen so as to substantially improve its practical performance compared to existing accelerated variants while at the same time preserve the optimal iteration-complexity shared by these methods. Computational results are … Read more

THE MULTI–FACILITY LOCATION PROBLEM: A PROBABILISTIC DECOMPOSITION METHOD

A generalized Weiszfeld method is proposed for the multi–facility location problem. The problem is relaxed using probabilistic assignments, and is decomposed into single facility location problems, that are coupled by these assignments, and can be solved in parallel. The probabilistic assignments are updated at each iteration, using the distances to the current centers. The method … Read more

Multi-horizon stochastic programming

Infrastructure-planning models are challenging because of their combination of different time scales: while planning and building the infrastructure involves strategic decisions with time horizons of many years, one needs an operational time scale to get a proper picture of the infrastructure’s performance and profitability. In addition, both the strategic and operational levels are typically subject … Read more

Complexity Analysis of Interior Point Algorithms for Non-Lipschitz and Nonconvex Minimization

We propose a first order interior point algorithm for a class of non-Lipschitz and nonconvex minimization problems with box constraints, which arise from applications in variable selection and regularized optimization. The objective functions of these problems are continuously differentiable typically at interior points of the feasible set. Our algorithm is easy to implement and the … Read more