Constrained Optimization Algorithm: Difference between revisions
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** an [[Interior Point Method]]. | ** an [[Interior Point Method]]. | ||
* <B>See:</B> [[Vector Space]], [[Constraint Rule]], [[Unconstrained Optimization Algorithm]], [[Geometric Optimality Conditions]], [[Fritz John Conditions]], [[Karush–Kuhn–Tucker Conditions]], [[Linear Programming]], [[Simplex Method]], [[Polynomial Time]], [[Quadratic Programming]], [[Ellipsoid Method]], [[Convex Function]]. | * <B>See:</B> [[Vector Space]], [[Constraint Rule]], [[Unconstrained Optimization Algorithm]], [[Geometric Optimality Conditions]], [[Fritz John Conditions]], [[Karush–Kuhn–Tucker Conditions]], [[Linear Programming]], [[Simplex Method]], [[Polynomial Time]], [[Quadratic Programming]], [[Ellipsoid Method]], [[Convex Function]]. | ||
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Latest revision as of 22:39, 16 June 2021
A Constrained Optimization Algorithm is an optimization algorithm that can be applied by an constrained optimization system (to solve a constrained optimization task).
- Example(s):
- a Lagrangian Method, such as ADMM.
- a Penalty Method.
- an Interior Point Method.
- See: Vector Space, Constraint Rule, Unconstrained Optimization Algorithm, Geometric Optimality Conditions, Fritz John Conditions, Karush–Kuhn–Tucker Conditions, Linear Programming, Simplex Method, Polynomial Time, Quadratic Programming, Ellipsoid Method, Convex Function.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/constrained_optimization#Solution_methods Retrieved:2015-11-8.
- Many unconstrained optimization algorithms can be adapted to the constrained case, often via the use of a penalty method. However, search steps taken by the unconstrained method may be unacceptable for the constrained problem, leading to a lack of convergence. This is referred to as the Maratos effect. [1]
1997
- (Luenberger, 1997) ⇒ David G. Luenberger. (1997). “Optimization by Vector Space Methods." Wiley Professional. ISBN:047118117X