Vector optimization
Vector optimization is a subarea of mathematical optimization where optimization problems with a vector-valued objective functions are optimized with respect to a given partial ordering and subject to certain constraints. A multi-objective optimization problem is a special case of a vector optimization problem: The objective space is the finite dimensional Euclidean space partially ordered by the component-wise "less than or equal to" ordering.
01Problem formulation
In mathematical terms, a vector optimization problem can be written as:
where for a partially ordered vector space
. The partial ordering is induced by a cone
.
is an arbitrary set and
is called the feasible set.
02Solution concepts
There are different minimality notions, among them:
is a weakly efficient point (weak minimizer) if for every
one has
.
is an efficient point (minimizer) if for every
one has
.
is a properly efficient point (proper minimizer) if
is a weakly efficient point with respect to a closed pointed convex cone
where
.
Every proper minimizer is a minimizer. And every minimizer is a weak minimizer.
Modern solution concepts not only consists of minimality notions but also take into account infimum attainment.
03Solution methods
- Benson's algorithm for linear vector optimization problems.
04Relation to multi-objective optimization
Any multi-objective optimization problem can be written as
where and
is the non-negative orthant of
. Thus the minimizer of this vector optimization problem are the Pareto efficient points.
Sources and credits
This article is adapted from the Wikipedia article “Vector optimization”, written by its contributors and licensed under CC BY-SA 4.0. Fathomly has changed the layout, removed citation markers, navigation and maintenance notices, and adjusted punctuation. This adapted version is shared under the same license. For references, see the original article.
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