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Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects,^{ [1]} where the set of feasible solutions is discrete or can be reduced to a discrete set. Typical combinatorial optimization problems are the travelling salesman problem ("TSP"), the minimum spanning tree problem ("MST"), and the knapsack problem. In many such problems, such as the ones previously mentioned, exhaustive search is not tractable, and so specialized algorithms that quickly rule out large parts of the search space or approximation algorithms must be resorted to instead.
Combinatorial optimization is related to operations research, algorithm theory, and computational complexity theory. It has important applications in several fields, including artificial intelligence, machine learning, auction theory, software engineering, VLSI, applied mathematics and theoretical computer science.
Some research literature^{ [2]} 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.^{[ clarification needed]}
Applications of combinatorial optimization include, but are not limited to:
There is a large amount of literature on polynomialtime algorithms for certain special classes of discrete optimization. A considerable amount of it is unified by the theory of linear programming. Some examples of combinatorial optimization problems that are covered by this framework are shortest paths and shortestpath trees, flows and circulations, spanning trees, matching, and matroid problems.
For NPcomplete discrete optimization problems, current research literature includes the following topics:
Combinatorial optimization problems can be viewed as searching for the best element of some set of discrete items; therefore, in principle, any sort of search algorithm or metaheuristic can be used to solve them. Perhaps the most universally applicable^{[ weasel words]} approaches are branchandbound (an exact algorithm which can be stopped at any point in time to serve as heuristic), branchandcut (uses linear optimisation to generate bounds), dynamic programming (a recursive solution construction with limited search window) and tabu search (a greedytype swapping algorithm). However, generic search algorithms are not guaranteed to find an optimal solution first, nor are they guaranteed to run quickly (in polynomial time). Since some discrete optimization problems are NPcomplete, such as the traveling salesman (decision) problem,^{ [7]} this is expected unless P=NP.
Formally, a combinatorial optimization problem is a quadruple^{[ citation needed]} , where
The goal is then to find for some instance an optimal solution, that is, a feasible solution with
For each combinatorial optimization problem, there is a corresponding decision problem that asks whether there is a feasible solution for some particular measure . For example, if there is a graph which contains vertices and , an optimization problem might be "find a path from to that uses the fewest edges". This problem might have an answer of, say, 4. A corresponding decision problem would be "is there a path from to that uses 10 or fewer edges?" This problem can be answered with a simple 'yes' or 'no'.
The field of approximation algorithms deals with algorithms to find nearoptimal solutions to hard problems. The usual decision version is then an inadequate definition of the problem since it only specifies acceptable solutions. Even though we could introduce suitable decision problems, the problem is then more naturally characterized as an optimization problem.^{ [8]}
This section may be
confusing or unclear to readers. In particular, the notation introduced in this section is not explained well and may not be standard. (December 2021) 
An NPoptimization problem (NPO) is a combinatorial optimization problem with the following additional conditions.^{ [9]} Note that the below referred polynomials are functions of the size of the respective functions' inputs, not the size of some implicit set of input instances.
This implies that the corresponding decision problem is in NP. In computer science, interesting optimization problems usually have the above properties and are therefore NPO problems. A problem is additionally called a Poptimization (PO) problem, if there exists an algorithm which finds optimal solutions in polynomial time. Often, when dealing with the class NPO, one is interested in optimization problems for which the decision versions are NPcomplete. Note that hardness relations are always with respect to some reduction. Due to the connection between approximation algorithms and computational optimization problems, reductions which preserve approximation in some respect are for this subject preferred than the usual Turing and Karp reductions. An example of such a reduction would be Lreduction. For this reason, optimization problems with NPcomplete decision versions are not necessarily called NPOcomplete.^{ [10]}
NPO is divided into the following subclasses according to their approximability:^{ [9]}
An NPO problem is called polynomially bounded (PB) if, for every instance and for every solution , the measure is bounded by a polynomial function of the size of . The class NPOPB is the class of NPO problems that are polynomiallybounded.