In discrete optimization, some or all of the variables in a model are required to belong to a discrete set; this is in contrast to continuous optimization in which the variables are allowed to take on any value within a range of values. Here, we consider two branches of discrete optimization. In integer programming, the discrete set is a subset of integers. In combinatorial optimization, the discrete set is a set of objects, or combinatorial structures, such as assignments, combinations, routes, schedules, or sequences.
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