Active-set method: Difference between revisions

Revision as of 21:12, 16 November 2013

Revision as of 21:21, 16 November 2013

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The active set is particularly important in optimization theory as it determines which constraints will influence the final result of optimization. For example, in solving the [[linear programming]] problem, the active set gives the hyperplanes that intersect at the solution point. In [[quadratic programming]], as the solution is not necessarily on one of the edges of the bounding polygon, an estimation of the active set gives us a subset of inequalities to watch while searching the solution, which reduces the complexity of the search.

==Active set methods==

In general an active set algorithm has the following structure:

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:'''end repeat'''

==See also==

Methods that can be described as '''active set methods''' include{{Citation needed}}:

* [[Successive linear programming]] (SLP) <!-- acc. to: Leyffer... - alt: acc. to "MPS glossary", http://glossary.computing.society.informs.org/ver2/mpgwiki/index.php/Main_Page: Successive approximation -->

* [[Sequential quadratic programming]] (SQP) <!-- acc. to: Leyffer... - alt: acc. to "MPS glossary", http://glossary.computing.society.informs.org/ver2/mpgwiki/index.php/Main_Page: Successive approximation -->