Methods for High-Dimensional Nonlinear Optimization

Abstract

Stochastic nonlinear optimization problems arise in a wide variety of data science applications. We propose a general algorithmic framework for solving optimization problems that are highdimensional; nonlinear (and possibly ill conditioned); nonconvex (and possibly ill posed). The proposed methods employ new sampling techniques, can take advantage of parallel architectures, and are supported by a convergence theory.

Document Details

Document Type
DoD Grant Award
Publication Date
Jan 23, 2018
Source ID
N000141812098

Entities

People

  • Jorge Nocedal

Organizations

  • Northwestern University
  • Office of Naval Research
  • United States Navy

Tags

Readers

  • Neural Network Machine Learning.
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms