A family of multi-parameterized proximal point algorithms

In this paper, a multi-parameterized proximal point algorithm combining with a relaxation step is developed for solving convex minimization problem subject to linear constraints. We show its global convergence and sublinear convergence rate from the prospective of variational inequality. Preliminary numerical experiments on testing a sparse minimization problem from signal processing indicate that the proposed … Read more

Migration from Sequence to Schedule in Total Earliness and Tardiness Scheduling Problem

Services must be delivered with high punctuality to be competitive. The classical scheduling theory offers to minimize the total earliness and tardiness of jobs to deliver punctual services. In this study, we developed a fully polynomial-time optimal algorithm to transform a given sequence, the permutation of jobs, into its corresponding minimum cost schedule, the timing … Read more

Optimal Control of Differential Inclusions

This paper is devoted to optimal control of dynamical systems governed by differential inclusions in both frameworks of Lipschitz continuous and discontinuous velocity mappings. The latter framework mostly concerns a new class of optimal control problems described by various versions of the so-called sweeping/Moreau processes that are very challenging mathematically and highly important in applications … Read more

Integer Programming Formulations for Minimum Spanning Tree Interdiction

We consider a two-player interdiction problem staged over a graph where the leader’s objective is to minimize the cost of removing edges from the graph so that the follower’s objective, i.e., the weight of a minimum spanning tree in the residual graph, is increased up to a predefined level $r$. Standard approaches for graph interdiction … Read more

Data-Driven Distributionally Robust Appointment Scheduling over Wasserstein Balls

We study a single-server appointment scheduling problem with a fixed sequence of appointments, for which we must determine the arrival time for each appointment. We specifically examine two stochastic models. In the first model, we assume that all appointees show up at the scheduled arrival times yet their service durations are random. In the second … Read more

ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs

We consider the embedding of piecewise-linear deep neural networks (ReLU networks) as surrogate models in mixed-integer linear programming (MILP) problems. A MILP formulation of ReLU networks has recently been applied by many authors to probe for various model properties subject to input bounds. The formulation is obtained by programming each ReLU operator with a binary … Read more

Geometric and Metric Characterizations of Transversality Properties

This paper continues the study of ‘good arrangements’ of collections of sets near a point in their intersection. Our aim is to clarify the relations between various quantitative geometric and metric characterizations of the transversality properties of collections of sets and the corresponding regularity properties of set-valued mappings. We expose all the parameters involved in … Read more

A Stochastic Bin Packing Approach for Server Consolidation with Conflicts

The energy consumption of large-scale data centers or server clusters is expected to grow significantly in the next couple of years contributing to up to 13 percent of the worlwide energy demand in 2030. As the involved processing units require a disproportional amount of energy when they are idle, underutilized or overloaded, balancing the supply … Read more

Nonlinear Transversality Properties of Collections of Sets: Dual Space Necessary Characterizations

This paper continues the study of ‘good arrangements’ of collections of sets in normed vector spaces near a point in their intersection. Our aim is to study general nonlinear transversality properties. We focus on dual space (subdifferential and normal cone) necessary characterizations of these properties. As an application, we provide dual necessary and sufficient conditions … Read more

HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search

The performance of deep neural networks is highly sensitive to the choice of the hyperparameters that define the structure of the network and the learning process. When facing a new application, tuning a deep neural network is a tedious and time consuming process that is often described as a “dark art”. This explains the necessity … Read more