Enriched Algorithms for Large Scale Unconstrained Optimization

Enriched Methods for Large-Scale Unconstrained Optimization

This paper describes a class of optimization methods that interlace iterations of the limited memory BFGS method L BFGS and a Hessian free Newton method HFN in such a way that the information collected by one type of iteration improves the performance of the other Curvature information about the objective function is stored in the form of a limited memory matrix and plays the dual role of preco...

Large Scale Unconstrained Optimization

This paper reviews advances in Newton quasi Newton and conjugate gradi ent methods for large scale optimization It also describes several packages developed during the last ten years and illustrates their performance on some practical problems Much attention is given to the concept of partial separa bility which is gaining importance with the arrival of automatic di erentiation tools and of opt...

Parallel Algorithms for Large Scale Nonlinear Unconstrained Optimization

A limited memory adaptive trust-region approach for large-scale unconstrained optimization

This study concerns with a trust-region-based method for solving unconstrained optimization problems. The approach takes the advantages of the compact limited memory BFGS updating formula together with an appropriate adaptive radius strategy. In our approach, the adaptive technique leads us to decrease the number of subproblems solving, while utilizing the structure of limited memory quasi-Newt...

Performance of Enriched Methods for Large Scale Unconstrained Optimization as applied to Models of Proteins

Energy minimization plays an important role in structure determination and analysis of proteins, peptides and other organic molecules; therefore, development of efficient minimization algorithms is important. Recently Morales and Nocedal have developed enriched methods for large scale unconstrained optimization that interlace iterations of the limited memory BFGS method (L-BFGS) and the Hessian...