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Continuous optimization problem

The standard form of a continuous optimization problem is¹ minimize x f ( x ) s u b j e c t t o g i ( x ) ≤ 0 , i = 1 , … , m h j ( x ) = 0 , j = 1 , … , p {\displaystyle {\begin{aligned}&{\underset {x}{\operatorname {minimize} }}&&f(x)\\&\operatorname {subject\;to} &&g_{i}(x)\leq 0,\quad i=1,\dots ,m\\&&&h_{j}(x)=0,\quad j=1,\dots ,p\end{aligned}}} where

• f : ℝn → ℝ is the objective function to be minimized over the n-variable vector x,
• gi(x) ≤ 0 are called inequality constraints
• hj(x) = 0 are called equality constraints, and
• m ≥ 0 and p ≥ 0.

If m = p = 0, 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

Main article: Combinatorial optimization

Formally, a combinatorial optimization problem A is a quadruple (I, f, m, g), where

• I is a set of instances;
• given an instance x ∈ I, f(x) is the set of feasible solutions;
• given an instance x and a feasible solution y of x, m(x, y) denotes the measure of y, which is usually a positive real.
• g is the goal function, and is either min or max.

The goal is then to find for some instance x an optimal solution, that is, a feasible solution y with m ( x , y ) = g { m ( x , y ′ ) : y ′ ∈ f ( x ) } . {\displaystyle m(x,y)=g\left\{m(x,y'):y'\in f(x)\right\}.}

For each combinatorial optimization problem, there is a corresponding decision problem that asks whether there is a feasible solution for some particular measure m0. For example, if there is a graph G which contains vertices u and v, an optimization problem might be "find a path from u to v 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 u to v 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.²

See also

• Counting problem (complexity) – Type of computational problem
• Design Optimization
• Ekeland's variational principle – theorem that asserts that there exist nearly optimal solutions to some optimization problemsPages displaying wikidata descriptions as a fallback
• Function problem – Type of computational problem
• Glove problem
• Operations research – Discipline concerning the application of advanced analytical methods
• Satisficing – Cognitive heuristic of searching for an acceptable decision − the optimum need not be found, just a "good enough" solution.
• Search problem – type of computational problem represented by a binary relationPages displaying wikidata descriptions as a fallback
• Semi-infinite programming – optimization problem with a finite number of variables and an infinite number of constraints, or an infinite number of variables and a finite number of constraintsPages displaying wikidata descriptions as a fallback

External links

• "How Traffic Shaping Optimizes Network Bandwidth". IPC. 12 July 2016. Retrieved 13 February 2017.

References

1. Boyd, Stephen P.; Vandenberghe, Lieven (2004). Convex Optimization (pdf). Cambridge University Press. p. 129. ISBN 978-0-521-83378-3. 978-0-521-83378-3 ↩

2. Ausiello, Giorgio; et al. (2003), Complexity and Approximation (Corrected ed.), Springer, ISBN 978-3-540-65431-5 978-3-540-65431-5 ↩