Modeling an Augmented Lagrangian for Blackbox Constrained Optimization

Modeling an Augmented Lagrangian for Blackbox Constrained Optimization

Modeling an Augmented Lagrangian for Improved Blackbox Constrained Optimization

Constrained blackbox optimization is a difficult problem, with most approaches coming from the mathematical programming literature. The statistical literature is sparse, especially in addressing problems with nontrivial constraints. This situation is unfortunate because statistical methods have many attractive properties: global scope, handling noisy objectives, sensitivity analysis, and so for...

An adaptive augmented Lagrangian method for large-scale constrained optimization

We propose an augmented Lagrangian algorithm for solving large-scale constrained optimization problems. The novel feature of the algorithm is an adaptive update for the penalty parameter motivated by recently proposed techniques for exact penalty methods. This adaptive updating scheme greatly improves the overall performance of the algorithm without sacrificing the strengths of the core augment...

An augmented Lagrangian trust region method for equality constrained optimization

In this talk, we present a trust region method for solving equality constrained optimization problems, which is motivated by the famous augmented Lagrangian function. It is different from standard augmented Lagrangian methods where the augmented Lagrangian function is minimized at each iteration. This method, for fixed Lagrange multiplier and penalty parameters, tries to minimize an approximate...

An Augmented Lagrangian Approach to Constrained MAP Inference An Augmented Lagrangian Aproach to Constrained MAP Inference

In this section, we derive in detail the closed form solution of problem (12) for binary pairwise factors (Sect. 4.1). Recall that the marginal polytope M(G a) is given by: