Textbooks
From SparseSolver
See also Other resources.
Contents |
Sparse-approximation and compressed-sensing textbooks and monographs
- Compressive Sensing and Structured Random Matrices monograph by Holger Rauhut, May 2010. Free.
- Compressed Sensing: Theory and Applications, ed. by Y. Eldar and G. Kutyniok (2012), Cambridge University press. It appears one of the introductory chapters by Gita Kutyniok is freely available.
- Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity by J.-L. Starck, F. Murtagh, and J. Fadili (2010). Cambridge University Press.
- Theoretical Foundations and Numerical Methods for Sparse Recovery by M. Fornasier (2010), published by De Gruyter.
- Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing by M. Elad (2010). Available on Springer-link.
- Statistics for High-Dimensional Data: Methods, Theory and Applications by Peter Buhlmann and Sara van de Geer (2011). Emphasis on the LASSO. Free for Springer-link subscribers.
Optimization textbooks
Free online optimization textbooks
- Convex Optimization by S. Boyd and L. Vandenberghe (2004). Very popular book, divided into (1) basic convex analysis, (2) applications, and (3) algorithms. More focus on semi-definite programming compared to other text-books.
- Convex Analysis and Nonlinear Optimization: Theory and Examples, J. Borwein and A. Lewis, 1999. Free online version. They also have a 2005 version which is not free, but it is available via Springer-link.
- Elements of Statistical Learning: Data mining, Inference and Prediction by Hastie, Tibshirani and Friedman.
- Supplementary Chapter 6 on Convex Optimization Algorithms by Dimitri Bertsekas, which is a free supplement to his Convex Optimization Theory book (Athena Scientific, 2009). This chapter is updated regularly.
- Gradient-Based Algorithms with Applications to Signal Recovery Problems book chapter by Amir Beck and Marc Teboulle, part of Convex Optimization in Signal Processing and Communications ed. by D. Palomar and Y. Eldar, Cambridge U. Press 2010. Covers modern first-order methods. Very readable and useful.
- Parallel and Distributed Computation: Numerical Methods by D. Bertsekas and J. Tsitsiklis (1997). This is now freely available online at MIT (it is scanned, so very large PDF files).
Conventional textbooks
Some of these are available online if you or your institution has the appropriate access (e.g. to Springerlink or SIAM e-books)
- Numerical Optimization by Nocedal and Wright, 2nd edition 2006. You can read online if you have Springerlink. This book is quite popular and is quite comprehensive.
- Nonlinear Programing by Bertsekas (1999), 2004 printing. Also a popular book.
- Convex Analysis and Optimization by Bertsekas (2003).
- Linear and Nonlinear Programming by David Luenberger and Yinyu Ye (3rd ed., 2008). You can read online via Springerlink if you have access.
- Numerical Optimization by Bonnans, Gilbert, Lemarechal and Sagastizabal (2nd ed., 2006). You can read online via Springerlink if you have access.
- Handbook of Semidefinite Programming ed. by H. Wolkowicz, R. Saigal, and L. Vandenberghe (2000), with typos and the online bibliography.
- Introductory Lectures on Convex Optimization: A Basic Course by Nesterov (2003). A short book, and a different approach. Very readable, and very influential (it is the modern reference for accelerated "Nesterov-type" methods).
- Lectures on Modern Convex Optimization: analysis, algorithms and engineering applications by A. Ben-tal and A. Nemirovski (2001). Available on SIAM e-Books if you have access.
- Interior-Point Polynomial Algorithms in Convex Programming by Yurii Nesterov and Arkadii Nemirovskii (1994). The influential book that introduced self-concordancy. Not often used as a main text-book though.
- Primal-Dual Interior-Point Methods by Stephen Wright (1997). Very readable explanation of state-of-the-art IPM; useful even if you want to extend IPM beyond LP. With the publishing of this book, the IPM field was officially "mature". Available on SIAM e-books if you have access.
- A Mathematical View of Interior-Point Methods in Convex Optimization by James Renegar (2001). Available online if you have SIAM e-books. Short book, not for undergraduates.
- Optimization Theory and Methods by Wenyu Sun and Ya-Xiang Yuan (2006). Available via Springerlink.
- Introduction to Optimization and Semidifferential Calculus by M. C. Delfour (2012). "This book is intended as a textbook for a one-term course at the undergraduate level...". Available online if you have SIAM e-books.
- Trust-region Methods by Andrew Conn, Nicholas Gould, and Ph. Toint (2000). Monograph on trust-region methods. "Written primarily for postgraduates and researchers, the book features an extensive commented bibliography..." Available online if you have SIAM e-books.
