Bibliography
- Barvinok, A. (2002). A course in convexity. Vol. 54 of Graduate studies in mathematics (American Mathematical Society).
- Ben-Tal, A. and Nemirovski, A. (2001). Lectures on Modern Convex Optimization (Society for Industrial and Applied Mathematics).
- Bertsimas, D.; Gupta, V. and Kallus, N. (2018). Data-driven robust optimization. Mathematical Programming 167, 235–292.
- Betts, J. T. (2010). Practical Methods for Optimal Control and Estimation Using Nonlinear Programming. Second Edition (Society for Industrial and Applied Mathematics).
- Boyd, S. and Vandenberghe, L. (2004). Convex Optimization (Cambridge University Press, Cambridge).
- Bukhsh, W. A.; Grothey, A.; McKinnon, K. I. and Trodden, P. A. (2013). Local Solutions of the Optimal Power Flow Problem. IEEE Transactions on Power Systems 28, 4780–4788.
- Cornuéjols, G.; Peña, J. and Tütüncü, R. (2018). Optimization Methods in Finance. 2 Edition (Cambridge University Press).
- D’Aertrycke, G.; Ehrenmann, A.; Ralph, D. and Smeers, Y. (2017). Risk trading in capacity equilibrium models (Cambridge Working Papers in Economics (CWPE)).
- Ferris, M. C.; Mangasarian, O. L. and Wright, S. J. (2007). Linear Programming with MATLAB (Society for Industrial and Applied Mathematics).
- Goemans, M. X. and Williamson, D. P. (1995). Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming. J. ACM 42, 1115–1145.
- Jabr, R. A. (2012). Exploiting Sparsity in SDP Relaxations of the OPF Problem. IEEE Transactions on Power Systems 27, 1138–1139.
- Knuth, D. E. (1994). The sandwich theorem. The Electronic Journal of Combinatorics 1.
- Krasko, V. and Rebennack, S. (2017). Global Optimization: Optimal Power Flow Problem. In: Advances and Trends in Optimization with Engineering Applications, edited by Terlaky, T.; Anjos, M. F. and Ahmed, S. (Society for Industrial and Applied Mathematics, Philadelphia, PA); Chapter 15, pp. 187–205.
- Linial, N. (2002). Finite Metric Spaces: Combinatorics, Geometry and Algorithms. In: Proceedings of the Eighteenth Annual Symposium on Computational Geometry, SCG '02 (Association for Computing Machinery, New York, NY, USA); p. 63.
- Matoušek, J. (2013). Lectures on discrete geometry. Vol. 212 no. 1 of Graduate Texts in Mathematics (Springer Science & Business Media).
- Peng, J. and Wei, Y. (2007). Approximating K‐means‐type Clustering via Semidefinite Programming. SIAM Journal on Optimization 18, 186–205.
- Zimmerman, R. D.; Murillo-Sánchez, C. E. and Thomas, R. J. (2011). MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education. IEEE Transactions on Power Systems 26, 12–19.