Optimization Letters最新文献

The purpose of this paper is to investigate a new inertial self-adaptive iterative algorithm for solving variational inequalities over the solution set of the split variational inequality problem with multiple output sets in real Hilbert spaces. The strong convergence result is given under some mild conditions widely used in the convergence analysis. Our algorithm is accelerated by the inertial technique and eliminates the dependence on the norm of the transformation operators and the strongly monotone and Lipschitz continuous constants of the involved operator. Two applications in the network bandwidth allocation problem and the image classification problem are shown, and some numerical experiments are presented to show the advantages of the proposed algorithm.

We investigate a two-machine job shop scheduling problem with optional job rejection. The target is to look for a feasible schedule for the set of accepted jobs so that the sum of the makespan of the accepted jobs and the total penalty of the rejected jobs is minimized. We propose an exact pseudo-polynomial dynamic programming algorithm, a greedy (frac{sqrt{5}+1}{2})-approximation algorithm, an LP-based (frac{e}{e-1})-approximation algorithm, and a fully polynomial time approximation scheme to solve it. We demonstrate that the proportionate case is (mathcal{N}mathcal{P})-hard and provide an (O(n^2))-time algorithm for the agreeable case.

Since it is computationally expensive to solve the vehicle routing problem (VRP) optimally, as this problem is NP-hard, in this technical note we study how to accurately approximate the optimal VRP tour length. In our previous papers, we developed a linear regression model including the mean and standard deviation of the modified Clarke and Wright heuristic solution values, which was able to predict the optimal VRP tour length fairly well. In this note, we find that by doing a small amount of extra work to include the minimum of the modified Clarke and Wright heuristic solution values, we can improve the predictive results substantially.

In this paper, we consider stochastic monotone Nash games where each player’s strategy set is characterized by possibly a large number of explicit convex constraint inequalities. Notably, the functional constraints of each player may depend on the strategies of other players, allowing for capturing a subclass of generalized Nash equilibrium problems (GNEP). While there is limited work that provide guarantees for this class of stochastic GNEPs, even when the functional constraints of the players are independent of each other, the majority of the existing methods rely on employing projected stochastic approximation (SA) methods. However, the projected SA methods perform poorly when the constraint set is afflicted by the presence of a large number of possibly nonlinear functional inequalities. Motivated by the absence of performance guarantees for computing the Nash equilibrium in constrained stochastic monotone Nash games, we develop a single timescale randomized Lagrangian multiplier stochastic approximation method where in the primal space, we employ an SA scheme, and in the dual space, we employ a randomized block-coordinate scheme where only a randomly selected Lagrangian multiplier is updated. We show that our method achieves a convergence rate of (mathcal {O}left( frac{log (k)}{sqrt{k}}right)) for suitably defined suboptimality and infeasibility metrics in a mean sense.

Problems related to projections on closed convex cones are frequently encountered in optimization theory and related fields. To study these problems, various unifying ideas have been introduced, including asymmetric vector-valued norms and certain generalized lattice-like operations. We propose a new perspective on these studies by describing how the problem of cone projection can be formulated using an order-theoretic formalism developed in this paper. The underlying mathematical structure is a partially ordered vector space that generalizes the notion of a vector lattice by using two partial orderings and having certain lattice-type properties with respect to these orderings. In this note we introduce a generalization of these so-called mixed lattice spaces, and we show how such structures arise quite naturally in some of the applications mentioned above.

The maximum independent set (MIS) seeks to find a subset of vertices with the maximum size such that no pair of its vertices are adjacent. This paper develops a recursive fixing procedure that generalizes the existing polytime algorithm to solve the maximum independent set problem on chordal graphs, which admit simplicial orderings. We prove that the generalized fixing procedure is safe; i.e., it does not remove all optimal solutions of the MIS problem from the solution space. Our computational results show that the proposed recursive fixing algorithm, along with the basic mixed integer programming (MIP) of the MIS, outperforms the pure MIP formulation of the problem. Our codes, data, and results are available on GitHub.