Alfonso: Matlab Package for Nonsymmetric Conic Optimization

References

• Andersen ED (2002) Handling free variables in primal-dual interior-point methods using a quadratic cone. Proc. SIAM Conf. Optim. (SIAM, Philadelphia).Google Scholar
• Ben-Tal A, Nemirovski A (2001) Lectures on Modern Convex Optimization (SIAM, Philadelphia).Crossref, Google Scholar
• Blekherman G, Parrilo PA, Thomas RR, eds. (2013) Semidefinite Optimization and Convex Algebraic Geometry (SIAM, Philadelphia).Google Scholar
• Boyd SP, Vandenberghe L (2004) Convex Optimization (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Chares R (2009) Cones and interior-point algorithms for structured convex optimization involving powers and exponentials. Unpublished PhD thesis, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.Google Scholar
• Coey C, Kapelevich L, Vielma JP (2020) Toward practical generic conic optimization. Preprint, submitted May 3, https://arxiv.org/abs/2005.01136.Google Scholar
• Domahidi A, Chu E, Boyd S (2013) ECOS: An SOCP solver for embedded systems. Proc. Eur. Control Conf. (ECC) (IEEE, Piscataway, NJ), 3071–3076.Google Scholar
• Glineur F, Terlaky T (2004) Conic formulation for lp-norm optimization. J. Optim. Theory Appl. 122(2):285–307.Crossref, Google Scholar
• Güler O (1997) Hyperbolic polynomials and interior point methods for convex programming. Math. Oper. Res. 22(2):350–377.Link, Google Scholar
• Iliman S, de Wolff T (2016) Amoebas, nonnegative polynomials and sums of squares supported on circuits. Res. Math. Sci. 3(1):1–35.Google Scholar
• Karimi M, Tunçel L (2019) Domain-driven solver (DDS): A MATLAB-based software package for convex optimization problems in domain-driven form. Preprint, submitted August 7, https://arxiv.org/abs/1908.03075.Google Scholar
• Mosek ApS (2019) Mosek Optimization Suite release 9.0.105. Accessed April 1 2021, https://docs.mosek.com/9.0/releasenotes/index.html.Google Scholar
• Nesterov Y, Nemirovskii A (1994) Interior-Point Polynomial Algorithms in Convex Programming, SIAM Studies in Applied Mathematics, vol. 13 (Society for Industrial and Applied Mathematics, Philadelphia).Crossref, Google Scholar
• O’Donoghue B, Chu E, Parikh N, Boyd S (2016) Operator splitting for conic optimization via homogeneous self-dual embedding. J. Optim. Theory Appl. 169:1042–1068.Google Scholar
• Papp D (2019) Duality of sum of nonnegative circuit polynomials and optimal SONC bounds. Preprint, submitted December 10, https://arxiv.org/abs/1912.04718.Google Scholar
• Papp D, Yıldız S (2017) On “A homogeneous interior-point algorithm for non-symmetric convex conic optimization.” Preprint, submitted December 1, https://arxiv.org/abs/1712.00492.Google Scholar
• Papp D, Yıldız S (2019) Sum-of-squares optimization without semidefinite programming. SIAM J. Optim. 29(1):822–851.Google Scholar
• Renegar J (2001) A Mathematical View of Interior-Point Methods in Convex Optimization, MOS-SIAM Series on Optimization (Society for Industrial and Applied Mathematics, Philadelphia).Crossref, Google Scholar
• Renegar J (2004) Hyperbolic programs, and their derivative relaxations. Technical report, Cornell University Operations Research and Industrial Engineering, Ithaca, NY.Google Scholar
• Roy S, Xiao L (2018) On self-concordant barriers for generalized power cones. Technical Report MSR-TR-2018-3, Microsoft Research, Redmond, WA.Google Scholar
• Skajaa A, Ye Y (2015) A homogeneous interior-point algorithm for nonsymmetric convex conic optimization. Math. Programming 150(2):391–422.Google Scholar