A scalable bounding method for multi-stage stochastic integer programs

Many dynamic decision problems involving uncertainty can be appropriately modeled as multi-stage stochastic programs. However, most practical instances are so large and/or complex that it is impossible to solve them on a single computer, especially due to memory limitations. Extending the work of Sandikci et al. (2013) on two-stage stochastic mixed-integer-programs (SMIPs), this paper develops … Read more

A Non-Parametric Structural Hybrid Modeling Approach for Electricity Prices

We develop a stochastic model of zonal/regional electricity prices, designed to reflect information in fuel forward curves and aggregated capacity and load as well as zonal or regional price spreads. We use a nonparametric model of the supply stack that captures heat rates and fuel prices for all generators in the market operator territory, combined … Read more

A Parallel Local Search Framework for the Fixed-Charge Multicommodity Network Flow Problem

We present a parallel local search approach for obtaining high quality solutions to the Fixed Charge Multi-commodity Network Flow problem (FCMNF). The approach proceeds by improving a given feasible solution by solving restricted instances of the problem where flows of certain commodities are fixed to those in the solution while the other commodities are locally … Read more

How Difficult is Nonlinear Optimization? A Practical Solver Tuning Approach, with Illustrative Results

Nonlinear optimization (NLO) per definitionem covers a vast range of problems, from trivial to practically intractable. For this reason, it is impossible to offer “guaranteed” advice to NLO software users. This fact becomes especially obvious, when facing unusually hard and/or previously unexplored NLO challenges. In the present study we offer some related practical observations, propose … Read more

Strict Fejér Monotonicity by Superiorization of Feasibility-Seeking Projection Methods

We consider the superiorization methodology, which can be thought of as lying between feasibility-seeking and constrained minimization. It is not quite trying to solve the full fledged constrained minimization problem; rather, the task is to find a feasible point which is superior (with respect to the objective function value) to one returned by a feasibility-seeking … Read more

A several new mixed integer linear programming formulations for exploration of online social networks

The goal of this paper is to identify the most promising sets of closest assignment constraints from the literature, in order to improve mixed integer linear programming formulations for exploration of information flow within a social network. The direct comparison between proposed formulations is performed on standard single source capacitated facility location problem instances. Therefore, … Read more

Parallel Large-Neighborhood Search Techniques for LNG Inventory Routing

Liquefied natural gas (LNG) is estimated to account for a growing portion of the world natural gas trade. For profitable operation of a capital intensive LNG project, it is necessary to optimally design various aspects of the supply chain associated with it. Of particular interest is optimization of ship schedules and the inventories on the … Read more

Parallel Algorithms for Big Data Optimization

We propose a decomposition framework for the parallel optimization of the sum of a differentiable function and a (block) separable nonsmooth, convex one. The latter term is usually employed to enforce structure in the solution, typically sparsity. Our framework is very flexible and includes both fully parallel Jacobi schemes and Gauss-Seidel (i.e., sequential) ones, as … Read more

CBLIB 2014: A benchmark library for conic mixed-integer and continuous optimization

The Conic Benchmark Library (CBLIB 2014) is a collection of more than a hundred conic optimization instances under a free and open license policy. It is the first extensive benchmark library for the advancing field of conic mixed-integer and continuous optimization, which is already supported by all major commercial solvers and spans a wide range … Read more

A search for quantum coin-flipping protocols using optimization techniques

Coin-flipping is a cryptographic task in which two physically separated, mistrustful parties wish to generate a fair coin-flip by communicating with each other. Chailloux and Kerenidis (2009) designed quantum protocols that guarantee coin-flips with near optimal bias away from uniform, even when one party deviates arbitrarily from the protocol. The probability of any outcome in … Read more