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Convergence Criteria 2: N-D Applications

Excerpt

This chapter builds on Chapter 6 and elaborates on convergence criteria that can be used in optimization. The concepts can be classified as either deterministic or stochastic and as either single player or multiplayer applications. Single trial solution searches (such as NR, LM, CHD, etc.) have one trial point and seek to move it. Here, convergence is based on the sequential trial solution, point-to-point changes. Multiplayer searches (such as LF and PSO) and pattern-type optimizers (such as HJ and NM) have a cluster of trial solutions. Here, convergence would be based on the range of the cluster or pattern.

This chapter builds on Chapter 6 and elaborates on convergence criteria that can be used in optimization. The concepts can be classified as either deterministic or stochastic and as either single player or multiplayer applications. Single trial solution searches (such as NR, LM, CHD, etc.) have one trial point and seek to move it. Here, convergence is based on the sequential trial solution, point-to-point changes. Multiplayer searches (such as LF and PSO) and pattern-type optimizers (such as HJ and NM) have a cluster of trial solutions. Here, convergence would be based on the range of the cluster or pattern.

17.1Introduction
17.2Defining an Iteration
17.3Criteria for Single TS Deterministic Procedures
17.4Criteria for Multiplayer Deterministic Procedures
17.5Stochastic Applications
17.6Miscellaneous Observations
17.7Takeaway
17.8Exercises
17.1Introduction
17.2Defining an Iteration
17.3Criteria for Single TS Deterministic Procedures
17.4Criteria for Multiplayer Deterministic Procedures
17.5Stochastic Applications
17.6Miscellaneous Observations
17.7Takeaway
17.8Exercises

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