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Are you up to it? This method is applied to a certain optimization problem when its derivatives are unavailable or unreliable.

Derivative-free methods establish a model based on sample function values or directly draw a sample set of function values without exploiting a detailed model.

Since it needs no derivatives, it cannot be compared to derivative-based methods. Some methods cannot handle optimization problems with more than a few variables; the results are usually not so accurate.

However, there are numerous practical cases where derivative-free methods have been successful in non-trivial simulation optimization problems that include randomness manifesting as "noise" in the objective function.

See, for example, the following [5] [11]. Derivative-free optimization methods are relatively simple and easy, but, like most optimization methods, some care is required in practical implementation e.

Dynamic programming deals with situations where decisions are made in stages. The key to this kind of problems is to trade off the present and future costs.

Neuro-dynamic programming is the same as dynamic programming except that the former has the concept of approximation architectures. It combines artificial intelligence , simulation-base algorithms, and functional approach techniques.

It means learning how to make improved decisions for the future via built-in mechanism based on the current behavior.

The most important part of neuro-dynamic programming is to build a trained neuro network for the optimal problem. Simulation based optimization has some limitations, such as the difficulty of creating a model that imitates the dynamic behavior of a system in a way that is considered good enough for its representation.

Another problem is complexity in the determining uncontrollable parameters of both real-world system and simulation. Moreover, only a statistical estimation of real values can be obtained.

It is not easy to determine the objective function, since it is a result of measurements, which can be harmful for the solutions.

From Wikipedia, the free encyclopedia. IEEE Computer Society, Handbook of Simulation Optimization. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control.

Hoboken: Wiley. Artificial Intelligence in Medicine. Approximate Dynamic Programming Solving the Curses of Dimensionality 2nd ed.

Introduction to Derivative-Free Optimization. MPS-SIAM Book Series on Optimization. Philadelphia: SIAM. Retrieved

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