Optimization-Based modeling with applications to transport: part 2. the optimization algorithm

Authors:
Joseph Young;Denis Ridzal;Pavel Bochev
Affiliations:
Numerical Analysis and Applications, Sandia National Laboratories, Albuquerque, NM;Optimization and Uncertainty Quantification, Sandia National Laboratories, Albuquerque, NM;Numerical Analysis and Applications, Sandia National Laboratories, Albuquerque, NM
Venue:
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
Year:
2011

Citing 5
Cited 3

Incremental remapping as a transport&slash;advection algorithm

Journal of Computational Physics
A Reflective Newton Method for Minimizing a Quadratic Function Subject to Bounds on some of the Variables

SIAM Journal on Optimization
Formulation, analysis and numerical study of an optimization-based conservative interpolation (remap) of scalar fields for arbitrary Lagrangian-Eulerian methods

Journal of Computational Physics
Optimization---Based modeling with applications to transport: part 1. abstract formulation

LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
Optimization-Based modeling with applications to transport: part 3. computational studies

LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing

Optimization---Based modeling with applications to transport: part 1. abstract formulation

LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
Optimization-Based modeling with applications to transport: part 3. computational studies

LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
Optimization-based remap and transport: A divide and conquer strategy for feature-preserving discretizations

Journal of Computational Physics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is the second of three related articles that develop and demonstrate a new optimization-based framework for computational modeling. The framework uses optimization and control ideas to assemble and decompose multiphysics operators and to preserve their fundamental physical properties in the discretization process. One application of the framework is in the formulation of robust algorithms for optimization-based transport (OBT). Based on the theoretical foundations established in Part 1, this paper focuses on the development of an efficient optimization algorithm for the solution of the remap subproblem that is at the heart of OBT.