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In applied mathematics and theoretical computer science, combinatorial optimization is a topic that consists of finding an optimal object from a finite set of objects.〔Schrijver, p. 1〕 In many such problems, exhaustive search is not feasible. It operates on the domain of those optimization problems, in which the set of feasible solutions is discrete or can be reduced to discrete, and in which the goal is to find the best solution. Some common problems involving combinatorial optimization are the traveling salesman problem ("TSP") and the minimum spanning tree problem ("MST"). Combinatorial optimization is a subset of mathematical optimization that is related to operations research, algorithm theory, and computational complexity theory. It has important applications in several fields, including artificial intelligence, machine learning, mathematics, auction theory, and software engineering. Some research literature〔(【引用サイトリンク】 url=http://www.elsevier.com/locate/disopt )〕 considers discrete optimization to consist of integer programming together with combinatorial optimization (which in turn is composed of optimization problems dealing with graph structures) although all of these topics have closely intertwined research literature. It often involves determining the way to efficiently allocate resources used to find solutions to mathematical problems. ==Applications== Applications for combinatorial optimization include, but are not limited to: * Developing the best airline network of spokes and destinations * Deciding which taxis in a fleet to route to pick up fares * Determining the optimal way to deliver packages * Determining the right attributes of concept elements prior to concept testing 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「combinatorial optimization」の詳細全文を読む スポンサード リンク
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