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  1. PEBBL: An object-oriented framework for scalable parallel branch and bound.

    Abstract not provided.
  2. PANTHER. Pattern ANalytics To support High-performance Exploitation and Reasoning.

    Sandia has approached the analysis of big datasets with an integrated methodology that uses computer science, image processing, and human factors to exploit critical patterns and relationships in large datasets despite the variety and rapidity of information. The work is part of a three-year LDRD Grand Challenge called PANTHER (Pattern ANalytics To support High-performance Exploitation and Reasoning). To maximize data analysis capability, Sandia pursued scientific advances across three key technical domains: (1) geospatial-temporal feature extraction via image segmentation and classification; (2) geospatial-temporal analysis capabilities tailored to identify and process new signatures more efficiently; and (3) domain- relevant models of humanmore » perception and cognition informing the design of analytic systems. Our integrated results include advances in geographical information systems (GIS) in which we discover activity patterns in noisy, spatial-temporal datasets using geospatial-temporal semantic graphs. We employed computational geometry and machine learning to allow us to extract and predict spatial-temporal patterns and outliers from large aircraft and maritime trajectory datasets. We automatically extracted static and ephemeral features from real, noisy synthetic aperture radar imagery for ingestion into a geospatial-temporal semantic graph. We worked with analysts and investigated analytic workflows to (1) determine how experiential knowledge evolves and is deployed in high-demand, high-throughput visual search workflows, and (2) better understand visual search performance and attention. Through PANTHER, Sandia's fundamental rethinking of key aspects of geospatial data analysis permits the extraction of much richer information from large amounts of data. The project results enable analysts to examine mountains of historical and current data that would otherwise go untouched, while also gaining meaningful, measurable, and defensible insights into overlooked relationships and patterns. The capability is directly relevant to the nation's nonproliferation remote-sensing activities and has broad national security applications for military and intelligence- gathering organizations.« less
  3. Modeling Mathematical Programs with Equilibrium Constraints in Pyomo

    We describe new capabilities for modeling MPEC problems within the Pyomo modeling software. These capabilities include new modeling components that represent complementar- ity conditions, modeling transformations for re-expressing models with complementarity con- ditions in other forms, and meta-solvers that apply transformations and numeric optimization solvers to optimize MPEC problems. We illustrate the breadth of Pyomo's modeling capabil- ities for MPEC problems, and we describe how Pyomo's meta-solvers can perform local and global optimization of MPEC problems.
  4. Modeling and Optimization of Block-Composable Mathematical Programs using Coopr.

  5. SGOPT User Manual Version 2.0

    This document provides a user manual for the SGOPT software library. SGOPT is a C++ class library for nonlinear optimization. This library uses an object-oriented design that allows the software to be extended to a new problem domains. Furthermore, this library was designed to that the interface is straightforward while providing flexibility to allow new algorithms to be easily added to this library. The SGOPT library has been used by several software projects at Sandia, and it is integrated into the DAKOTA design and analysis toolkit. This report provides a high-level description of the optimization algorithms provided by SGOPT andmore » describes the C++ class hierarchy in which they are implemented. Finally, installation instructions are included.« less
  6. C++ LIBRARY OF ALOGRITHMS FOR STOCHASTIC GLOBAL OPTIMIZATION

    SGOPT is a C++ library that includes implementations of several algorithms for stochastic global optimization and derivative free optimization.
  7. What?s New in Coopr: Modeling Complex Systems with Blocks.

  8. Critiquing Coopr?s Modeling and Analysis Environment.

  9. Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization

    The authors describe a convergence theory for evolutionary pattern search algorithms (EPSAs) on a broad class of unconstrained and linearly constrained problems. EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. The analysis significantly extends the previous convergence theory for EPSAs. The analysis applies to a broader class of EPSAs,and it applies to problems that are nonsmooth, have unbounded objective functions, and which are linearly constrained. Further, they describe a modest change to the algorithmic framework ofmore » EPSAs for which a non-probabilistic convergence theory applies. These analyses are also noteworthy because they are considerably simpler than previous analyses of EPSAs.« less
  10. LDRD Final Report: Global Optimization for Engineering Science Problems

    For a wide variety of scientific and engineering problems the desired solution corresponds to an optimal set of objective function parameters, where the objective function measures a solution's quality. The main goal of the LDRD ''Global Optimization for Engineering Science Problems'' was the development of new robust and efficient optimization algorithms that can be used to find globally optimal solutions to complex optimization problems. This SAND report summarizes the technical accomplishments of this LDRD, discusses lessons learned and describes open research issues.
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"Hart, William E."

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