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For unconstrained numerical optimization I have been using the book "Numerical Methods for Unconstrained Optimization and Nonlinear Equations" by Dennis and Schnabel. I found it to be a great book (thanks J.M. for the suggestion) and fared very well with it. Now I'm wondering if there is such an easy (!) to understand book for constrained optimization. It should cover topics like:

  • Inner points methods
  • Penalty methods (exact and multiple)
  • SQP methods (including SQP-Trust-Regions)
  • Active sets strategies

And maybe nonsmooth optimization:

  • Moreau Yosida regularization
  • proximal point method
  • Tikhonov regularization
  • subgradient method

Especially important for me, is a good and easy coverage of the SQP algorithm.

Thank you for your time!

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    Vandenberghe's UCLA 236c notes are a good resource, especially for the nonsmooth optimization topics and for interior point methods: http://www.seas.ucla.edu/~vandenbe/ee236c.html2018-12-30

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One that does some, but not all of what you ask is Bazaraa, Sherali and Shetty (1993) Nonlinear programming theory and applications. 2nd ed. John Wiley and Sons.

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I had a similar problem two years ago. My solution: this book. The bad part is that it is in german, but the good part is that it is the most complete book I have seen on the topics you mention, at least for a semester course. Maybe this is not a solution for you, so I think you will find in this book exactly what you are looking for. Best wishes!

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Numerical Optimization, by Nocedal and Wright, covers both unconstrained and constrained optimization. Searching for this book on Amazon leads to other books on optimization.