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Nonlinear Optimization Examples

Options Vector

The options vector, represented by the "opt" argument, enables you to specify a variety of options, such as the amount of printed output or particular update or line-search techniques. Table 11.2 gives a summary of the available options.

Table 11.2: Summary of the Elements of the Options Vector
Index Description
1specifies minimization, maximization, or the number of least-squares functions
2specifies the amount of printed output
3NLPDD, NLPLM, NLPNRA, NLPNRR, NLPTR: specifies the scaling of the Hessian matrix (HESCAL)
4NLPCG, NLPDD, NLPHQN, NLPQN: specifies the update technique (UPDATE)
5NLPCG, NLPHQN, NLPNRA, NLPQN (with no nonlinear constraints): specifies the line-search technique (LIS)
6NLPHQN: specifies version of hybrid algorithm (VERSION)
 NLPQN with nonlinear constraints: specifies version of \mu update
7NLPDD, NLPHQN, NLPQN: specifies initial Hessian matrix (INHESSIAN)
8Finite Difference Derivatives: specifies type of differences and how to compute the difference interval
9NLPNRA: specifies the number of rows returned by the sparse Hessian module
10NLPNMS, NLPQN: specifies the total number of constraints returned by the "nlc" module
11NLPNMS, NLPQN: specifies the number of equality constraints returned by the "nlc" module

The following list contains detailed explanations of the elements of the options vector:

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