Environments for Large-Scale Optimization
Ali Bouaricha and Jorge J. Moré
We are developing an environment (ELSO) for large
scale optimization problems that only
requires the user to provide code to evaluate
a partially separable function.
This novel approach eliminates the need to provide the
gradient and sparsity pattern;
in all other approaches the user is required to provide the
gradient and (for a Newton method) the sparsity pattern.
This will provide a unique capability that is not
The research on environment for large
scale optimization is a joint project of
Argonne National Laboratory
and the NSF
Center for Research on Parallel Computation
The schematic below shows that in a typical situation the user provides
computes the gradient and sparsity
pattern, and our trust region Newton method solves the problem.
Recent Papers and Technical Reports
A. Bouaricha and Jorge J. Moré,
Impact of partial separability on large-scale optimization,
Preprint MCS-P487-0195, November 1995.
- Christian H. Bischof, Ali Bouaricha,
Peyvand Khademi, and Jorge J. Moré,
Computing gradients in large-scale optimization
using automatic differentiation,
Preprint MCS-P488-0195, January 1995.
- Brett M. Averick, and Jorge J. Moré,
Evaluation of large-scale optimization problems on
vector and parallel architectures.
SIAM Journal on Optimization, 4 (1994), 708-721.
- Brett M. Averick, Christian H. Bischof,
Alan Carle, Jorge J. Moré, and Andreas Griewank,
Computing large sparse Jacobian matrices using automatic differentiation,
SIAM Journal on Scientific and Statistical Computing, 15 (1994), 285-294.
Argonne National Laboratory / email@example.com