Simulation-based optimization: Difference between revisions

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=== Derivative-free optimization methods ===

[[Derivative-free optimization]] is a subject of mathematical optimization. This method is applied to a certain optimization problem when its derivatives are unavailable or unreliable. Derivate-free method establishes model based on sample function values or directly draw a sample set of function values without exploiting detailed model. Since it needs no derivatives, it cannot be compared to derivative-based methods.<ref>Conn, A. R.; Scheinberg, K.; Vicente, L. N. (2009). [http://www.mat.uc.pt/~lnv/idfo/ ''Introduction to Derivative-Free Optimization'']. MPS-SIAM Book Series on Optimization. Philadelphia: SIAM. Retrieved 2014-01-18.</ref>

For unconstrained optimization problems, it has athe form:

:<math>\underset{\text{x}\in\R^n}{\min}f\bigl(\text{x}\bigr)</math>

The limitationlimitations of derivative-free optimization:

1. It is usually cannot handle optimization problems with a few tens of variables,; the results via this method are usually not so accurate.

2. When confronted with minimizing non-convex functions, it will show its limitation.

3. Derivative-free optimization methods isare simple and easy,; however, itthey isare not so good in theory and in practice.

=== Dynamic programming and neuro-dynamic programming ===