Introduction
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Summarizes, in tabular form, the functions available for minimization, equation solving, and solving least-squares or data fitting problems. It also provides basic guidelines for using the optimization routines and introduces the algorithms and line-search strategies that are available for solving medium- and large-scale problems.
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Examples that Use Standard Algorithms
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Presents medium-scale algorithms through a selection of minimization examples. These examples include unconstrained and constrained problems, as well as problems with and without user-supplied gradients. This section also discusses maximization, greater-than-zero constraints, passing additional arguments, and multiobjective examples.
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Large-Scale Examples
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Presents large-scale algorithms through a selection of large-scale examples. These examples include specifying sparsity structures, and preconditioners, as well as unconstrained and constrained problems.
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Default Parameter Settings
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Describes the use of default parameter settings and tells you how to change them. It also tells you how to determine which parameters are used by a specified function, and provides examples of setting some commonly used parameters.
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Displaying Iterative Output
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Describes the column headings used in the iterative output of both medium-scale and large-scale algorithms.
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Optimization of Inline Objects Instead of M-Files
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Tells you how to represent a mathematical function at the command line by creating an inline object from a string expression.
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Typical Problems and How to Deal with Them
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Provides tips to help you improve solutions found using the optimization functions, improve efficiency of the algorithms, overcome common difficulties, and transform problems that are typically not in standard form.
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Converting Your Code to Version 2 Syntax
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Compares a Version 1.5 call to the equivalent Version 2 call for each function. This section also describes the Version 2 calling sequences and provides a detailed example of converting from constr to its replacement fmincon.
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Selected Bibliography
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Lists published materials that support concepts implemented in the Optimization Toolbox.
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