K Best Solutions of Combinatorial Optimization Problems
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A combinatorial optimization problem is a problem in which one has to           choose among a limited number of alternatives the optimal one, i.e.,            one which maximizes profit, or minimizes cost, etc.  Although the               number of alternatives is limited, it is in general too large to                enumerate all of them and to compare the values measuring their                 benefit.  Therefore, combinatorial optimization develops algorithms to          find the optimal alternative without having to know all of them.  In            this research, this idea is extended to the case of finding not only            the best, but also the first K best solutions.  In a real-world                 situation modeled by a combinatorial optimization problem, the best             solution may not be available, or the information about the 2nd,                3rd,..., and K the alternative is essential in deciding which of the            alternatives should be implemented.  From a theoretical point of view,          the research will lead to new results regarding the solution of                 so-called NP-complete problems, i.e., combinatorial problems, the               optimal solution of which can only be approximated rather than                  computed.  In most of these problems, an approach will be used which            computes the K best solutions of a simpler problem until the K best             solution is feasible for the original problem.
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{'Code': '145400', 'Text': 'SYSTEMS THEORY'}
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