Optimization Tips and Tricks 1.0

New users and old of optimization in MATLAB will find useful tips and tricks in this document, as well as examples one can use as templates for their own problems.Use this tool by editing the file optimtips.m, then execute blocks of code in cell mode from the editor, or best, publish the file to HTML. Copy and paste also works of course.Some readers may find this tool valuable if only for the function pleas - a partitioned least squares solver based on lsqnonlin.This is a work in progress, as I fully expect to add new topics as I think of them or as suggestions are made. Suggestions for topics I've missed are welcome, as are corrections of my probable numerous errors. The topics currently covered are listed below.Contents1. Linear regression basics in matlab2. Polynomial regression models3. Weighted regression models4. Robust estimation5. Ridge regression6. Transforming a nonlinear problem to linearity7. Sums of exponentials8. Poor starting values9. Before you have a problem10. Tolerances & stopping criteria11. Common optimization problems & mistakes12. Partitioned least squares estimation13. Errors in variables regression14. Passing extra information/variables into an optimization15. Minimizing the sum of absolute deviations16. Minimize the maximum absolute deviation17. Batching small problems into large problems18. Global solutions & domains of attraction19. Bound constrained problems20. Inclusive versus exclusive bound constraints21. Mixed integer/discrete problems22. Understanding how they work23. Wrapping an optimizer around quad24. Graphical tools for understanding sets of nonlinear equations25. Optimizing non-smooth or stochastic functions26. Linear equality constraints27. Sums of squares surfaces and the geometry of a regression28. Confidence limits on a regression model29. Confidence limits on the parameters in a nonlinear regression30. Quadprog example, unrounding a curve31. R^232. Estimation of the parameters of an implicit function33. Robust fitting schemes34. Homotopies35. Orthogonal polynomial regression36. Potential topics to be added or expanded in the (near) future

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Attraction,  Bound,  Constrained,  Constraints,  Domains,  Exclusive,  Global,  Inclusive,  Informationvariables,  Integerdiscrete,  Mixed,  Optimizer,  Problems,  Solutions,  Understanding,  Versus,  Work,  Wrapping
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