On Mixed-Integer Optimal Control with Constrained Total Variation of the Integer Control

The combinatorial integral approximation (CIA) decomposition suggests to solve mixed-integer optimal control problems (MIOCPs) by solving one continuous nonlinear control problem and one mixed-integer linear program (MILP). Unrealistic frequent switching can be avoided by adding a constraint on the total variation to the MILP. Within this work, we present a fast heuristic way to solve … Read more

Multivariable branching: A 0-1 knapsack problem case study

We explore the benefits of multi-variable branching strategies for linear programming based branch and bound algorithms for the 0-1 knapsack problem, i.e., of branching on sets of variables rather than on a single variable (the current default in integer programming solvers). We present examples where multi-variable branching shows advantage over single-variable branching, and partially characterize … Read more

Stochastic Optimization Models of Insurance Mathematics

The paper overviews stochastic optimization models of insurance mathematics and methods for their solution from the point of view of stochastic programming and stochastic optimal control methodology, with vector optimality criteria. The evolution of an insurance company’s capital is considered in discrete time. The main random variables, which influence this evolution, are levels of payments, … Read more

Spectral Gap Optimization of Divergence Type Diffusion Operators

In this paper, we address the problem of maximizing the spectral gap of a divergence type diffusion operator. Our main application of interest is characterizing the distribution of a swarm of agents that evolve on a bounded domain in Rn according to a Markov process. A subclass of the divergence type operators that we introduce … Read more

A unified convergence theory for Non monotone Direct Search Methods (DSMs) with extensions \ to DFO with mixed and categorical variables

This paper presents a unified convergence theory for non monotonous Direct Search Methods (DSMs), which embraces several algorithms that have been proposed for the solution of unconstrained and boxed constraints models. This paper shows that these models can be theoretically solved with the same methodology and under the same weak assumptions. All proofs have a … Read more

Tree Bounds for Sums of Bernoulli Random Variables: A Linear Optimization Approach

We study the problem of computing the tightest upper and lower bounds on the probability that the sum of n dependent Bernoulli random variables exceeds an integer k. Under knowledge of all pairs of bivariate distributions denoted by a complete graph, the bounds are NP-hard to compute. When the bivariate distributions are specified on a … Read more

New MINLP Formulations for the Unit Commitment Problems with Ramping Constraints

The Unit Commitment (UC) problem in electrical power production requires to optimally operate a set of power generation units over a short time horizon (one day to a week). Operational constraints of each unit depend on its type (e.g., thermal, hydro, nuclear, …), and can be rather complex. For thermal units, typical ones concern minimum … Read more

A counterexample to an exact extended formulation for the single-unit commitment problem

Recently, Guan, Pan, and Zohu presented a MIP model for the thermal single- unit commitment claiming that provides an integer feasible solution for any convex cost function. In this note we provide a counterexample to this statement and we produce evidence that the perspective function is needed for this aim. Citation Research Report 19-03, Istituto … Read more

Gamma-Robust Linear Complementarity Problems with Ellipsoidal Uncertainty Sets

We study uncertain linear complementarity problems (LCPs), i.e., problems in which the LCP vector q or the LCP matrix M may contain uncertain parameters. To this end, we use the concept of Gamma-robust optimization applied to the gap function formulation of the LCP. Thus, this work builds upon [16]. There, we studied Gamma-robustified LCPs for … Read more

Inexact proximal stochastic second-order methods for nonconvex composite optimization

In this paper, we propose a framework of Inexact Proximal Stochastic Second-order (IPSS) methods for solving nonconvex optimization problems, whose objective function consists of an average of finitely many, possibly weakly, smooth functions and a convex but possibly nons- mooth function. At each iteration, IPSS inexactly solves a proximal subproblem constructed by using some positive … Read more