I am involved in the following ongoing software development projects:
- DAKOTA
- Summary: The DAKOTA (Design
Analysis Kit for Optimization and Terascale
Applications) toolkit provides a flexible,
extensible interface between analysis codes and
iterative systems analysis methods. DAKOTA
contains algorithms for optimization with
gradient and nongradient-based methods;
uncertainty quantification with sampling,
reliability, and stochastic finite element
methods; parameter estimation with nonlinear
least squares methods; and sensitivity/variance
analysis with design of experiments and parameter
study capabilities. These capabilities may be
used on their own or as components within
advanced strategies such as surrogate-based
optimization, mixed integer nonlinear
programming, or optimization under
uncertainty.
- Contributions:
surrogate-based optimization,
constraint relaxation for infeasible starting points,
AMPL interface,
DAKOTA-Coliny integration and testing
- Info: Version 4.0 released May 12, 2006
- Availability: GPL Download
- Trilinos
- Summary:
The Trilinos Project is an effort to develop
parallel solver algorithms and libraries within
an object-oriented software framework for the
solution of large-scale, complex multi-physics
engineering and scientific applications. A unique
design feature of Trilinos is its focus on
packages.
- Contributions:
preconditioners for space-time (XYZT) iterative solution of transient PDEs
- Info: Version 7.0 to be released Fall 2006
- Availability: LGPL Download
- QCS
- Summary:
The QCS (Query, Cluster, Summarize) information
retrieval (IR) system is a tool for querying,
clustering, and summarizing generic document
sets. QCS has been developed as a modular
development framework, and thus facilitates the
inclusion of new technologies targeting these
three IR tasks
- Contributions: Lead developer
- Info: A demo of version 1.0 is available
- Availability: TBD
- HOPE
- Summary:
A MATLAB implementation of HOPE (homotopy
optimization using perturbations and ensembles)
This method differs from previous homotopy and
continuation methods in that its aim is to find a
minimizer for each of a set of values of the
homotopy parameter, rather than to follow a path
of minimizers. To increase the probability of
finding global optima, HOPE can follow an
ensemble of points obtained by perturbation of
previous ones at each value of the continuation
parameter .
- Contributions: Lead developer
- Info: Contact dmdunla@sandia.gov
about becoming a beta tester
- Availability: TBD
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