Optimization Algorithm: Difference between revisions
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** a [[Minimum Mean Squared Error Algorithm]], | ** a [[Minimum Mean Squared Error Algorithm]], | ||
** a [[Minimum Chi-Square Estimation Algorithm]]. | ** a [[Minimum Chi-Square Estimation Algorithm]]. | ||
* <B>See:</B> [[Greedy Algorithm]], | * <B>See:</B> [[Greedy Algorithm]], [[Statistical Inference]], [[Parameter Estimation]], [[Local Maximum]], [[Absolute Maximum]]. | ||
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Revision as of 03:42, 12 July 2023
An Optimization Algorithm is a search algorithm that can be applied by a optimization system (to solve an optimization task).
- Context:
- It can range from being a Combinatorial Optimization Algorithm to being a Continuous Optimization Algorithm.
- It can range from being a Global Optimization Algorithm to being a Local Optimization Algorithm.
- It can range from being an Offline Optimization Algorithm to being an Online Optimization Algorithm.
- It can range from being an Exact Optimization Algorithm to being an Approximate Optimization Algorithm, depending on the task's optimality guarantees.
- It can range from being a Single-Variable Optimization Algorithm to being a Multi-Variable Optimization Algorithm (MVO).
- It ranges from being a Maximization Algorithm to being a Minimization Algorithm.
- Example(s):
- Counter-Example(s):
- See: Greedy Algorithm, Statistical Inference, Parameter Estimation, Local Maximum, Absolute Maximum.