Optimization problem

In mathematics, engineering, computer science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions.

Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete:

• An optimization problem with discrete variables is known as a discrete optimization, in which an object such as an integer, permutation or graph 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 problems and multimodal problems.

Continuous optimization problem

The standard form of a continuous optimization problem is $$\begin{array}{cccc}& \underset{x}{\mathrm{minimize}}& & f(x)\\ & \mathrm{s}\mathrm{u}\mathrm{b}\mathrm{j}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{t}\mathrm{o}& & g_i(x)\le 0,i=1,\dots ,m\\ & & & h_j(x)=0,j=1,\dots ,p\end{array}$$ where

• Template:Math is the objective function to be minimized over the Template:Mvar-variable vector Template:Mvar,
• Template:Math are called inequality constraints
• Template:Math are called equality constraints, and
• Template:Math and Template:Math.

If Template:Math, 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 Template:Mvar is a quadrupleTemplate:Citation needed Template:Math, where

• Template:Math is a set of instances;
• given an instance Template:Math, Template:Math is the set of feasible solutions;
• given an instance Template:Mvar and a feasible solution Template:Mvar of Template:Mvar, Template:Math denotes the measure of Template:Mvar, which is usually a positive real.
• Template:Mvar is the goal function, and is either Template:Math or Template:Math.

The goal is then to find for some instance Template:Mvar an optimal solution, that is, a feasible solution Template:Mvar with $$m(x,y)=g\{m(x,y^{\prime }):y^{\prime }\in f(x)\}.$$

For each combinatorial optimization problem, there is a corresponding decision problem that asks whether there is a feasible solution for some particular measure Template:Math. For example, if there is a graph Template:Mvar which contains vertices Template:Mvar and Template:Mvar, an optimization problem might be "find a path from Template:Mvar to Template:Mvar 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 Template:Mvar to Template:Mvar that uses 10 or fewer edges?" This problem can be answered with a simple 'yes' or 'no'.

In the field of approximation algorithms, 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.

See also

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• Template:Annotated link − the optimum need not be found, just a "good enough" solution.
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External links

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