OR/MA/ST706 References

OR/MA/ST 706: References

Textbook

D. G. Luenberger and Y. Ye, “Linear and Nonlinear Programming,” 4th Edition, 2016, Springer, ISBN 978-3-319-18842-3 (eBook)

References

  1. Apostol, T.M., 1974. Mathematical Analysis, 2nd ed., Reading, Mass: Addison-Wesley.
  2. R.T. Rockafellar, Convex Analysis, Princeton University Press, ISBN: 0691080690, 1972.
  3. P.E. Gill, W. Murray, M.H. Wright: Practical Optimization, QA402.5 .G54, 1981.
  4. G.P. McCormick, Nonlinear Programming: Theory, Algorithms and Applications, T57.8 .M39, 1983.
  5. M.S. Bazaraa, H.D. Sherali and C.M. Shetty: Nonlinear Programming: Theory and Algorithms, T57.8 .B39, 1993.
  6. S. Boyd, L. Vandenberghe: Convex Optimization, QA402.5 .B69, 2004.
  7. A. Nemirovski. Lectures on Modern Convex Optimization, ISYE, Georgia Tech., 2005.
  8. E. G. Birgin and J. M. Martinez. Practical Augmented Lagrangian Methods for Constrained Optimization, Society of Industrial and Applied Mathematics, ISBN 978-1-611973-35-8, 2014.
  9. C.J. Goh, X.Q. Yang: Duality in Optimization and Variational Inequalities, ISBN: 0415274796, 2002.
  10. F. Facchinei, J.-S. Pang, Finite-Dimensional Variational Inequalities and Complementarity Problems, ISBN: 038795581X, 2003.
  11. S.-C. Fang, W. Xing. Linear Conic Programming, Science Press, ISBN: 9787030381767, 2013.
  12. A. Messac. Optimization in Practice with Matlab, Cambridge Univ. Press, ISBN: 9781107109186, 2015.
  13. P. Flach. Machine Learning, Cambridge University Press, ISBN 978-0-511973-00-0, 2012.
  14. SR. Gunn. Support vector machines for classification and regression. ISIS technical report. 1998 May 10;14(1):5-16.
  15. Charu C. Aggarwal, Neural Networks and Deep Learning, 2018, Springer, ISBN-13: 978-3319944623.