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Selective Optimization
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Title:
Selective Optimization
Author:
Ahmed, Shabbir
;
Dey, Santanu S
Subjects:
ALGORITHMS
;
COMBINATORIAL ANALYSIS
;
DECISION MAKING
;
DISTRIBUTED COMPUTING
;
FUNCTIONS(MATHEMATICS)
;
GAUSSIAN NOISE
;
LINEAR PROGRAMMING
;
MIP(MIXED-INTEGER PROGRAMMING)
;
NONLINEAR SYSTEMS
;
Operations Research
;
OPTIMIZATION
;
PERFORMANCE(ENGINEERING)
;
PROBABILITY
;
PROBLEM
VARIABLES
;
RANDOM
VARIABLES
;
SO(SELECTIVE OPTIMIZATION)
;
Statistics and Probability
;
STOCHASTIC PROCESSES
;
VECTOR ANALYSIS
Description:
This project focuses on developing algorithms for optimization problems that have intrinsic limitations preventing the utilization of all available decision alternatives (problem variables) and/or the satisfaction of all constraints. Part of the optimization decision in these problems is the selection of which variables to use and/or which subset of constraints to satisfy. We refer to these problems as selective optimization (SO) problems. The combinatorial aspects of selection make these problems extremely difficult. In this project we develop a set of generic tools applicable to a wide class of selective optimization problems. Our approach is based on standard mixed-integer programming (MIP) formulations of selective optimization problems.While such formulations can be attacked by commercial optimization solvers, they typically exhibit extremely poor performance. We develop a variety of effective model and algorithm enhancement techniques for the standardMIP formulations. These techniques are easily integrable into commercial MIP solvers, thereby making them readily usable in applications of selective optimization. The original document contains color images.
Creation Date:
2015
Language:
English
Source:
DTIC Technical Reports
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