K
K. A. Ariyawansa
Researcher at Washington State University
Publications - 35
Citations - 280
K. A. Ariyawansa is an academic researcher from Washington State University. The author has contributed to research in topics: Stochastic programming & Semidefinite programming. The author has an hindex of 8, co-authored 35 publications receiving 264 citations. Previous affiliations of K. A. Ariyawansa include University of Toronto.
Papers
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Journal ArticleDOI
On a New Collection of Stochastic Linear Programming Test Problems
K. A. Ariyawansa,Andrew J. Felt +1 more
TL;DR: An introduction to the stochastic linear program is provided, a brief description of each problem family currently in the test-problem collection is given, and the documentation that accompanies the collection is described.
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Deriving collinear scaling algorithms as extension of quasi-Newton methods and the local convergence of DFP-and BFGS-related collinear scaling algorithms
TL;DR: The results in this paper imply the local and q-superlinear convergence of collinear scaling algorithms of Sorensen (1982, pp. 154–156) related to the DFP and BFGS methods.
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Stochastic semidefinite programming: a new paradigm for stochastic optimization
K. A. Ariyawansa,Yuntao Zhu +1 more
TL;DR: Stochastic semidefinite programs are introduced as a paradigm for dealing with uncertainty in data defining semideFinite programs.
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Performance of a benchmark parallel implementation of the Van Slyke and wets algorithm for two-stage stochastic programs on the sequent/balance
K. A. Ariyawansa,D. D. Hudson +1 more
TL;DR: A benchmark parallel version of the Van Slyke and Wets algorithm for two-stage stochastic programs and an implementation of that algorithm on the Sequent/Balance are described and demonstrated, indicating that the benchmark implementation parallelizes well and that even with the use of parallel processing, problems with random variables having large numbers of realizations can take prohibitively large amounts of computation for solution.
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A preliminary set of applications leading to stochastic semidefinite programs and chance-constrained semidefinite programs
Yuntao Zhu,K. A. Ariyawansa +1 more
TL;DR: Results are promising and it is hoped that the applications presented in this paper would encourage researchers to consider SSDP and CCSDP as new paradigms for stochastic optimization when they formulate optimization models.