On the complexity of the steepest-descent with exact linesearches

The worst-case complexity of the steepest-descent algorithm with exact linesearches for unconstrained smooth optimization is analyzed, and it is shown that the number of iterations of this algorithm which may be necessary to find an iterate at which the norm of the objective function’s gradient is less that a prescribed $\epsilon$ is, essentially, a multiple … Read more

Gradient consistency for integral-convolution smoothing functions

Chen and Mangasarian (1995) developed smoothing approximations to the plus function built on integral-convolution with density functions. X. Chen (2012) has recently picked up this idea constructing a large class of smoothing functions for nonsmooth minimization through composition with smooth mappings. In this paper, we generalize this idea by substituting the plus function for an … Read more

A Quadratically Constrained Quadratic Optimization Model for Completely Positive Cone Programming

We propose a class of quadratic optimization problems whose exact optimal objective values can be computed by their completely positive cone programming relaxations. The objective function can be any quadratic form. The constraints of each problem are described in terms of quadratic forms with no linear terms, and all constraints are homogeneous equalities, except one … Read more

Second-order variational analysis and characterizations of tilt-stable optimal solutions in finite and infinite dimensions

The paper is devoted to developing second-order tools of variational analysis and their applications to characterizing tilt-stable local minimizers of constrained optimization problems in finite-dimensional and infinite-dimensional spaces. The importance of tilt stability has been well recognized from both theoretical and numerical aspects of optimization. Based on second-order generalized differentiation, we obtain qualitative and quantitative … Read more

On Stable Piecewise Linearization and Generalized Algorithmic Differentiation

It is shown how functions that are defined by evaluation programs involving the absolute value function (besides smooth elementals), can be approximated locally by piecewise-linear models in the style of algorithmic, or automatic, differentiation (AD). The model can be generated by a minor modification of standard AD tools and it is Lipschitz continuous with respect … Read more

Primal-dual relationship between Levenberg-Marquardt and central trajectories for linearly constrained convex optimization

We consider the minimization of a convex function on a compact polyhedron defined by linear equality constraints and nonnegative variables. We define the Levenberg-Marquardt (L-M) and central trajectories starting at the analytic center and using the same parameter, and show that they satisfy a primal-dual relationship, being close to each other for large values of … Read more

Dynamic sequencing and cut consolidation for the parallel hybrid-cut nested L-shaped method

The Nested L-shaped method is used to solve two- and multi-stage linear stochastic programs with recourse, which can have integer variables on the first stage. In this paper we present and evaluate a cut consolidation technique and a dynamic sequencing protocol to accelerate the solution process. Furthermore, we present a parallelized implementation of the algorithm, … Read more

Branch-and-Lift Algorithm for Deterministic Global Optimization in Nonlinear Optimal Control

This paper presents a branch-and-lift algorithm for solving optimal control problems with smooth nonlinear dynamics and nonconvex objective and constraint functionals to guaranteed global optimality. This algorithm features a direct sequential method and builds upon a spatial branch-and-bound algorithm. A new operation, called lifting, is introduced which refines the control parameterization via a Gram-Schmidt orthogonalization … Read more