, computer science
, an optimization problem is the problem
of finding the ''best'' solution from all feasible solution
Optimization problems can be divided into two categories, depending on whether the variables
* An optimization problem with discrete variables is known as a ''discrete optimization
'', in which an object
such as an integer
must be found from a countable set
* A problem with continuous variables is known as a ''continuous optimization
'', in which an optimal value from a continuous function
must be found. They can include constrained problem
s and multimodal problems.
Continuous optimization problem
The ''standard form
'' of a continuous
optimization problem is
* is the objective function
to be minimized over the -variable vector ,
* are called inequality constraints
* are called equality constraints, and
* and .
If , the problem is an unconstrained optimization problem. By convention, the standard form defines a minimization problem. A maximization problem can be treated by negating
the objective function.
Combinatorial optimization problem
Formally, a combinatorial optimization
problem is a quadruple , where
* is a set
* given an instance , is the set of feasible solutions;
* given an instance and a feasible solution of , denotes the measure
of , which is usually a positive real
* is the goal function, and is either or .
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'.
In the field of approximation algorithm
s, algorithms are designed to find near-optimal 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 more naturally characterized as an optimization problem.
*Counting problem (complexity)
: the optimum need not be found, just a "good enough" solution.