Activity Identification and Local Linear Convergence of Forward–Backward-type methods

In this paper, we consider a class of Forward–Backward (FB) splitting methods that includes several variants (e.g. inertial schemes, FISTA) for minimizing the sum of two proper convex and lower semi-continuous functions, one of which has a Lipschitz continuous gradient, and the other is partly smooth relatively to a smooth active manifold $\mathcal{M}$. We propose … Read more

Solving Classical and New Single Allocation Hub Location Problems on Euclidean Data

Transport networks with hub structure organise the exchange of shipments between many sources and sinks. All sources and sinks are connected to a small number of hubs which serve as transhipment points, so that only few, strongly consolidated transport relations exist. While hubs and detours lead to additional costs, the savings from bundling shipments, i.e. … Read more

Robust optimization based EV charging

With the introduction of new technologies like electric vehicles and smart grids the operation and planning of power systems are subject to major changes. These technologies can bring various ftexibilities to different entities involved in decision making. This paper proposes a robust optimization based method to optimal charging/discharging of electric vehicles con­ sidering the electricity … Read more

Performance of First- and Second-Order Methods for L1-Regularized Least Squares Problems

We study the performance of first- and second-order optimization methods for l1-regularized sparse least-squares problems as the conditioning and the dimensions of the problem increase up to one trillion. A rigorously defined generator is presented which allows control of the dimensions, the conditioning and the sparsity of the problem. The generator has very low memory … Read more

Smooth Strongly Convex Interpolation and Exact Worst-case Performance of First-order Methods

We show that the exact worst-case performance of fixed-step first-order methods for unconstrained optimization of smooth (possibly strongly) convex functions can be obtained by solving convex programs. Finding the worst-case performance of a black-box first-order method is formulated as an optimization problem over a set of smooth (strongly) convex functions and initial conditions. We develop … Read more

UFO 2014 – Interactive System for Universal Functional Optimization

This report contains a description of the interactive system for universal functional optimization UFO, version 2014. This version contains interfaces to the MATLAB and SCILAB graphics environments. Citation Research Report V1218-14, Institute of Computer Science, Czech Academy of Sciences, Prague 2014. Article Download View UFO 2014 – Interactive System for Universal Functional Optimization

On the equivalence of the method of conjugate gradients and quasi-Newton methods on quadratic problems

In this paper we state necessary and sufficient conditions for equivalence of the method of conjugate gradients and quasi-Newton methods on a quadratic problem. We show that the set of quasi-Newton schemes that generate parallel search directions to those of the method of conjugate gradients is strictly larger than the one-parameter Broyden family. In addition, … Read more

Discrete optimization methods to fit piecewise-affine models to data points

Fitting piecewise affine models to data points is a pervasive task in many scientific disciplines. In this work, we address the k- Piecewise Affine Model Fitting with Pairwise Linear Separability problem (k-PAMF-PLS) where, given a set of real points and the corresponding observations, we have to partition the real domain into k pairwise linearly separable … Read more

Complexity of Minimum Irreducible Infeasible Subsystem Covers for Flow Networks

For an infeasible network flow system with supplies and demands, we consider the problem of finding a minimum irreducible infeasible subsystem cover, i.e., a smallest set of constraints that must be dropped to obtain a feasible system. The special cases of covers which only contain flow balance constraints (node cover) or only flow bounds (arc … Read more

Parallelizing the dual revised simplex method

This paper introduces the design and implementation of two parallel dual simplex solvers for general large scale sparse linear programming problems. One approach, called PAMI, extends a relatively unknown pivoting strategy called suboptimization and exploits parallelism across multiple iterations. The other, called SIP, exploits purely single iteration parallelism by overlapping computational components when possible. Computational … Read more