One of the most efficient penalty based methods to solve constrained optimization problems is the augmented Lagrangian algorithm. This paper presents a constrained optimization algorithm to solve continuous constrained global optimization problems. The proposed algorithm integrates the benefit...

In this work we consider the network equilibrium problem formulated as convex minimization problem whose variables are the path flows. In order to take into account the difficulties related to the large dimension of real network problems we adopt a decomposition-based approach suitably combined...

We study the double discrete Legendre–Fenchel transform (LFT) to approximate the convex envelope of a given function. We analyze the convergence of the double discrete LFT in the multivariate case based on previous convergence results for the discrete LFT. We focus our attention on the grid on...

<Para ID="Par1">The mesh adaptive direct search (<Emphasis Type="SmallCaps">Mads) algorithm is designed for blackbox optimization problems subject to general inequality constraints. Currently, <Emphasis Type="SmallCaps">Mads does not support equalities, neither in theory nor in practice. The present work proposes extensions to treat problems with linear equalities...</emphasis></emphasis></para>

<Para ID="Par1">We describe the most recent evolution of our constrained and unconstrained testing environment and its accompanying SIF decoder. Code-named <Emphasis FontCategory="SansSerif">SIFDecode and <Emphasis FontCategory="SansSerif">CUTEst, these updated versions feature dynamic memory allocation, a modern thread-safe Fortran modular design, a new Matlab interface and a...</emphasis></emphasis></para>

This paper addresses a network pricing problem where users are assigned to the paths of a transportation network according to a mixed logit model, i.e., price sensitivity varies across the user population. For its solution, we propose algorithms based on combinatorial approximations, and show...

In this paper, we propose a structured trust-region algorithm combining with filter technique to minimize the sum of two general functions with general constraints. Specifically, the new iterates are generated in the Gauss-Seidel type iterative procedure, whose sizes are controlled by a...

The problem of finding the best rank-one approximation to higher-order tensors has extensive engineering and statistical applications. It is well-known that this problem is equivalent to a homogeneous polynomial optimization problem. In this paper, we study theoretical results and numerical...

Many researchers have used Oneshot optimization methods based on user-specified primal state iterations, the corresponding adjoint iterations, and appropriately preconditioned design steps. Our goal here is to develop heuristics for sequencing these three subtasks, in order to optimize the...