An ILP-based local search procedure for the VRP with pickups and deliveries

In this paper we address the Vehicle Routing Problem with Pickups and Deliveries (VRPPD), an extension of the classical Vehicle Routing Problem (VRP) where we consider precedences among customers, and develop an Integer Linear Programming (ILP) based local search procedure. We consider the capacitated one-to-one variant, where a particular precedence must be satisfied between pairs … Read more

The One-Dimensional Dynamic Dispatch Waves Problem

We study same-day delivery (SDD) distribution systems by formulating the Dynamic Dispatch Wave Problem (DDWP), which models a depot where delivery requests arrive dynamically throughout a service day. At any dispatch epoch (wave), the information available to the decision maker is (1) a set of known, open requests which remain unfulfilled, and (2) a set … Read more

A Nonmonotone Approach without Differentiability Test for Gradient Sampling Methods

Recently, optimization problems involving nonsmooth and locally Lipschitz functions have been subject of investigation, and an innovative method known as Gradient Sampling has gained attention. Although the method has shown good results for important real problems, some drawbacks still remain unexplored. This study suggests modifications to the gradient sampling class of methods in order to … Read more

A Comment on “Computational Complexity of Stochastic Programming Problems”

Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal work of Dyer and Stougie (Mathematical Programming A, 106(3):423–432, 2006). Amongst others, that paper argues that linear two-stage stochastic programs with fixed recourse are #P-hard even if the random problem data is … Read more

Second order forward-backward dynamical systems for monotone inclusion problems

We begin by considering second order dynamical systems of the from $\ddot x(t) + \Gamma (\dot x(t)) + \lambda(t)B(x(t))=0$, where $\Gamma: {\cal H}\rightarrow{\cal H}$ is an elliptic bounded self-adjoint linear operator defined on a real Hilbert space ${\cal H}$, $B: {\cal H}\rightarrow{\cal H}$ is a cocoercive operator and $\lambda:[0,+\infty)\rightarrow [0,+\infty)$ is a relaxation function depending … Read more

Global convergence of sequential injective algorithm for weakly univalent vector equation: application to regularized smoothing Newton algorithm

It is known that the complementarity problems and the variational inequality problems are reformulated equivalently as a vector equation by using the natural residual or Fischer-Burmeister function. In this short paper, we first study the global convergence of a sequential injective algorithm for weakly univalent vector equation. Then, we apply the convergence analysis to the … Read more

Real-Time Dispatchability of Bulk Power Systems with Volatile Renewable Generations

The limited predictability and high variability of renewable generations has brought significant challenges on the real-time operation of bulk power systems. This paper proposes the concept of real-time dispatchability (RTDA) of power systems with variable energy resources, which focuses on investigating the impact of operating constraints and the cost of corrective actions on the flexibility … Read more

Robust Testing for Causal Inference in Observational Studies

A vast number of causal inference studies use matching techniques, where treatment cases are matched with similar control cases. For observational data in particular, we claim there is a major source of uncertainty that is essentially ignored in these tests, which is the way the assignments of matched pairs are constructed. It is entirely possible, … Read more

Convergence rate of a proximal multiplier algorithm for separable convex minimization

The proximal multiplier method with proximal distances (PMAPD) proposed by O. Sarmiento C., E. A. Papa Quiroz and P. R. Oliveira, applied to solve a convex program with separable structure unified the works of Chen and Teboulle (PCPM method), Kyono and Fukushima (NPCPMM) and Auslender and Teboulle (EPDM) and extended the convergence properties for the … Read more

Parallel Block Coordinate Minimization with Application to Group Regularized Regression

This paper proposes a method for parallel block coordinate-wise minimization for convex functions. Each iteration involves a first phase where n independent minimizations are performed over the n variable blocks, followed by a phase where the results of the first phase are coordinated to obtain the whole variable update. Convergence of the method to the … Read more