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Enhancements to Optimizers

Excerpt

The objective is to improve the optimizers, to optimize the optimization algorithms. Specific metrics would be to maximize the probability of finding the global optimum and robustness to aberrations (constraints, nonlinearities, stochastic response, discontinuities, flat spots, etc.) while minimizing computational work (of both the algorithm and number of function evaluations) and minimizing complexity (for either programmer or user). In any one optimizer, there seem to be dozens of enhancements the lead to improvements.

The objective is to improve the optimizers, to optimize the optimization algorithms. Specific metrics would be to maximize the probability of finding the global optimum and robustness to aberrations (constraints, nonlinearities, stochastic response, discontinuities, flat spots, etc.) while minimizing computational work (of both the algorithm and number of function evaluations) and minimizing complexity (for either programmer or user). In any one optimizer, there seem to be dozens of enhancements the lead to improvements.

18.1Introduction
18.2Criteria for Replicate Trials
18.3Quasi-Newton
18.4Coarse–Fine Sequence
18.5Number of Players
18.6Search Range Adjustment
18.7Adjustment of Optimizer Coefficient Values or Options in Process
18.8Initialization Range
18.9OF and DV Transformations
18.10Takeaway
18.11Exercises
18.1Introduction
18.2Criteria for Replicate Trials
18.3Quasi-Newton
18.4Coarse–Fine Sequence
18.5Number of Players
18.6Search Range Adjustment
18.7Adjustment of Optimizer Coefficient Values or Options in Process
18.8Initialization Range
18.9OF and DV Transformations
18.10Takeaway
18.11Exercises

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