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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Models, Theoretical Properties, And Solution Approaches For Stochastic Programming With Endogenous Uncertainty, Tanveer Hossain Bhuiyan
Models, Theoretical Properties, And Solution Approaches For Stochastic Programming With Endogenous Uncertainty, Tanveer Hossain Bhuiyan
Doctoral Dissertations
In a typical optimization problem, uncertainty does not depend on the decisions being made in the optimization routine. But, in many application areas, decisions affect underlying uncertainty (endogenous uncertainty), either altering the probability distributions or the timing at which the uncertainty is resolved. Stochastic programming is a widely used method in optimization under uncertainty. Though plenty of research exists on stochastic programming where decisions affect the timing at which uncertainty is resolved, much less work has been done on stochastic programming where decisions alter probability distributions of uncertain parameters. Therefore, we propose methodologies for the latter category of optimization under …
Randomized Approximation Of The Constraint Satisfaction Problem, Hoong Chuin Lau, Osamu Watanabe
Randomized Approximation Of The Constraint Satisfaction Problem, Hoong Chuin Lau, Osamu Watanabe
Research Collection School Of Computing and Information Systems
We consider the Weighted Constraint Satisfaction Problem (W-CSP) which is a fundamental problem in Artificial Intelligence and a generalization of important combinatorial problems such as MAX CUT and MAX SAT. In this paper, we prove non-approximability properties of W-CSP and give improved approximations of W-CSP via randomized rounding of linear programming and semidefinite programming relaxations. Our algorithms are simple to implement and experiments show that they are run-time efficient.