Modeling and Solving Location Routing and Scheduling Problems

This paper studies location routing and scheduling problems, a class of problems in which the decisions of facility location, vehicle routing, and route assignment are optimized simultaneously. For a version with capacity and time restrictions, two formulations are presented, one graph-based and one set-partitioning-based. For the set-partitioning-based formulation, valid inequalities are identified and their effectiveness … Read more

On LP Relaxations for the Pattern Minimization Problem

We discuss two formulations of the Pattern Minimization Problem: (1) introduced by Vanderbeck, and (2) obtained adding setup variables to the cutting stock formulation by Gilmore-Gomory. Let $z_i^{LP}(u)$ be the bound given by the linear relaxation of ($i$) under a given vector $u = (u_k)$ of parameters. We show that $z_2^{LP}(u}) \ge z_1^{LP}(u)$ and provide … Read more

Necessary conditions for local optimality in d.c. programming

Using $\eps$-subdifferential calculus for difference-of-convex (d.c.) programming, D\”ur proposed a condition sufficient for local optimality, and showed that this condition is not necessary in general. Here it is proved that whenever the convex part is strongly convex, this condition is also necessary. Strong convexity can always be ensured by changing the given d.c. decomposition slightly. … Read more

Exploiting special structure in semidefinite programming: a survey of theory and applications

Semidefinite Programming (SDP) may be seen as a generalization of Linear Programming (LP). In particular, one may extend interior point algorithms for LP to SDP, but it has proven much more difficult to exploit structure in the SDP data during computation. We survey three types of special structure in SDP data: 1) a common `chordal’ … Read more

Improved bounds for interatomic distance in Morse clusters

We improve the best known lower bounds on the distance between two points of a Morse cluster in $\mathbb{R}^3$, with $\rho \in [4.967,15]$. Our method is a generalization of the one applied to the Lennard-Jones potential in~\cite{Schac}, and it also leads to improvements of lower bounds for the energy of a Morse cluster. Some of … Read more

A globally convergent primal-dual interior-point filter method for nonlinear programming: new filter optimality measures and computational results

In this paper we modify the original primal-dual interior-point filter method proposed in [18] for the solution of nonlinear programming problems. We introduce two new optimality filter entries based on the objective function, and thus better suited for the purposes of minimization, and propose conditions for using inexact Hessians. We show that the global convergence … Read more

Lipschitz behavior of the robust regularization

To minimize or upper-bound the value of a function “robustly”, we might instead minimize or upper-bound the “epsilon-robust regularization”, defined as the map from a point to the maximum value of the function within an epsilon-radius. This regularization may be easy to compute: convex quadratics lead to semidefinite-representable regularizations, for example, and the spectral radius … Read more