Browsing by Author "Padula, Anthony D."

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    A Software Framework for the Abstract Expression of Coordinate-Free Linear Algebra and Optimization Algorithms
    (2005-10) Symes, William W.; Padula, Anthony D.; Scott, Shannon D.
    Object oriented design solves a fundamental programming problem arising in scientific and engineering applications of linear algebra and optimization: the separation in code of multiple levels of abstraction naturally appearing in solution algorithms for such problems. The Rice Vector Library provides C++ classes expressing core concepts (vector, function,...) of calculus in Hilbert space with minimal implementation dependence, and standardized interfaces behind which to hide application-dependent implementation details (data containers, function objects). A variety of coordinate free algorithms from linear algebra and optimization, including Krylov subspace methods and various relatives of Newton's method for nonlinear equations and constrained and unconstrained optimization, may be expressed purely in terms of this system of classes. The resulting code may be used {\em without alteration} in a wide range of control, design, and parameter esti mation applications, in serial and parallel computing environments.
  • Thumbnail Image
    Item
    Designing and Analyzing Computational Experiments for Global Optimization
    (2000-07) Trosset, Michael W.; Padula, Anthony D.
    We consider a variety of issues that arise when designing and analyzing computational experiments for global optimization. We describe a probability model for objective functions and a method for generating pseudorandom objective functions. We argue in favor of evaluating the performance of global optimization algorithms by measuring the depth of the objective function achieved with a fixed number of function evaluations. We emphasize the importance of replication in computational experiments and describe some useful statistical techniques for assimilating results. We illustrate our methods by performing a small study that compares two multistart strategies for global optimization.
  • Thumbnail Image
    Item
    Software Design for Simulation Driven Optimization
    (2005-10) Padula, Anthony D.
    This thesis describes a flexible framework for abstract numerical algorithms which treats algorithms as objects and makes them reusable, composable, and modifiable. These algorithm objects are implemented using the Rice Vector Library (RVL) interface, decoupling the algorithmic code from the details of linear algebra and calculus in Hilbert Space. I made many improvements to the RVL design, including abstract return types for reductions. These improvements allowed me to demonstrate the breadth of this design by incorporating semantically similar objects from other packages which had significant syntatic differences to the RVL objects. By adapting other libraries, I gain access to a variety of tools, including parallel linear algebra implementations. The benefits of the algorithm framework can be seen when abstract numerical algorithms are coupled with parallel simulators without needing to modify either the algorithm or the simulator.
  • Thumbnail Image
    Item
    Software design for simulation driven optimization
    (2005) Padula, Anthony D.; Symes, William W.
    This thesis describes a flexible framework for abstract numerical algorithms which treats algorithms as objects and makes them reusable, composable, and modifiable. These algorithm objects are implemented using the Rice Vector Library (RVL) interface, decoupling the algorithmic code from the details of linear algebra and calculus in Hilbert Space. I made many improvements to the RVL design, including abstract return types for reductions. These improvements allowed me to demonstrate the breadth of this design by incorporating semantically similar objects from other packages which had significant syntatic differences to the RVL objects. By adapting other libraries, I gain access to a variety of tools, including parallel linear algebra implementations. The benefits of the algorithm framework can be seen when abstract numerical algorithms are coupled with parallel simulators without needing to modify either the algorithm or the simulator.