In this paper, we propose a new customized proximal point algorithm for linearly constrained convex optimization problem, and further use it to solve the separable convex optimization problem with linear constraints. Which is different to the existing customized proximal point algorithms, the proposed algorithm does not involve any relaxation step but still ensure the convergence. … Read more
Mathematical programs with equilibrium (or complementarity) constraints, MPECs for short, are a difficult class of constrained optimization problems. The feasible set has a very special structure and violates most of the standard constraint qualifications (CQs). Thus, the Karush-Kuhn-Tucker (KKT) conditions are not necessarily satisfied by minimizers and the convergence assumptions of many methods for solving … Read more
This paper discusses several (sub)gradient methods attaining the optimal complexity for smooth problems with Lipschitz continuous gradients, nonsmooth problems with bounded variation of subgradients, weakly smooth problems with H\”older continuous gradients. The proposed schemes are optimal for smooth strongly convex problems with Lipschitz continuous gradients and optimal up to a logarithmic factor for nonsmooth problems … Read more
We apply novel inner-iteration preconditioned Krylov subspace methods to the interior-point algorithm for linear programming (LP). Inner-iteration preconditioners recently proposed by Morikuni and Hayami enable us to overcome the severe ill-conditioning of linear equations solved in the final phase of interior-point iterations. The employed Krylov subspace methods do not suffer from rank-deficiency and therefore no … Read more
The International Space Station poses very challenging issues from the logistic point of view. Its on-orbit stay is to be significantly extended in the near future and ever increasing experimental activity in microgravity is expected, giving rise to a renewed interest in the related optimization aspects. A permanent logistic support is necessary to guarantee its … Read more
This chapter examines the problem of packing tetris-like items, orthogonally, with the possibility of rotations, into a convex domain, in the presence of additional conditions. An MILP (Mixed Integer Linear Programming) and an MINLP (Mixed Integer Nonlinear Programming) models, previously studied by the author, are surveyed. An efficient formulation of the objective function, aimed at … Read more
An $n\times n$ matrix $X$ is called completely positive semidefinite (cpsd) if there exist $d\times d$ Hermitian positive semidefinite {matrices} $\{P_i\}_{i=1}^n$ (for some $d\ge 1$) such that $X_{ij}= {\rm Tr}(P_iP_j),$ for all $i,j \in \{ 1, \ldots, n \}$. The cpsd-rank of a cpsd matrix is the smallest $d\ge 1$ for which such a representation … Read more
This work is inspired by very challenging issues arising in space logistics. The problem of scheduling a number of activities, in a given time elapse, optimizing the resource exploitation is discussed. The available resources are not constant, as well as the request, relative to each job. The mathematical aspects are illustrated, providing a time-indexed MILP … Read more
This paper describes how the cut-and-solve framework and semi-Lagrangean based dual ascent algorithms can be integrated in two natural ways in order to solve the single source capacitated facility location problem. The first uses the cut-and-solve framework both as a heuristic and as an exact solver for the semi-Lagrangean subproblems. The other uses a semi-Lagrangean … Read more
In a recent paper, Bubeck, Lee, and Singh introduced a new first order method for minimizing smooth strongly convex functions. Their geometric descent algorithm, largely inspired by the ellipsoid method, enjoys the optimal linear rate of convergence. Motivated by their work, we propose a close variant that iteratively maintains a quadratic global under-estimator of the … Read more