SDPA
SDPA (SemiDefinite Programming Algorithm)” is one of the most efficient and stable software packages for solving SDPs based on the primal-dual interior-point method. It fully exploits the sparsity of given problems. There are some variants of the SDPA;
Keywords for this software
References in zbMATH (referenced in 185 articles )
Showing results 1 to 20 of 185.
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- Gribling, Sander; Laurent, Monique; Steenkamp, Andries: Bounding the separable rank via polynomial optimization (2022)
- Rontsis, Nikitas; Goulart, Paul; Nakatsukasa, Yuji: Efficient semidefinite programming with approximate ADMM (2022)
- Arslan, Ali; Fantuzzi, Giovanni; Craske, John; Wynn, Andrew: Bounds on heat transport for convection driven by internal heating (2021)
- Dostert, Maria; de Laat, David; Moustrou, Philippe: Exact semidefinite programming bounds for packing problems (2021)
- Hauenstein, Jonathan D.; Liddell, Alan C. jun.; McPherson, Sanesha; Zhang, Yi: Numerical algebraic geometry and semidefinite programming (2021)
- Lidický, Bernard; Pfender, Florian: Semidefinite programming and Ramsey numbers (2021)
- Lourenço, Bruno F.; Muramatsu, Masakazu; Tsuchiya, Takashi: Solving SDP completely with an interior point oracle (2021)
- Magron, Victor; Safey El Din, Mohab: On exact Reznick, Hilbert-Artin and Putinar’s representations (2021)
- Massaccesi, G. E.; Rubio-García, A.; Capuzzi, P.; Ríos, E.; Oña, O. B.; Dukelsky, J.; Lain, L.; Torre, A.; Alcoba, D. R.: Variational determination of the two-particle reduced density matrix within the doubly occupied configuration interaction space: exploiting translational and reflection invariance (2021)
- Sekiguchi, Yoshiyuki; Waki, Hayato: Perturbation analysis of singular semidefinite programs and its applications to control problems (2021)
- Wang, Yuzhu; Tanaka, Akihiro; Yoshise, Akiko: Polyhedral approximations of the semidefinite cone and their application (2021)
- Cifuentes, Diego; Kahle, Thomas; Parrilo, Pablo: Sums of squares in Macaulay2 (2020)
- de Laat, David: Moment methods in energy minimization: new bounds for Riesz minimal energy problems (2020)
- Fantuzzi, Giovanni; Goluskin, David: Bounding extreme events in nonlinear dynamics using convex optimization (2020)
- Kobayashi, Ken; Takano, Yuich: A branch-and-cut algorithm for solving mixed-integer semidefinite optimization problems (2020)
- Permenter, Frank; Parrilo, Pablo A.: Dimension reduction for semidefinite programs via Jordan algebras (2020)
- Qian, Xun; Liao, Li-Zhi; Sun, Jie: A strategy of global convergence for the affine scaling algorithm for convex semidefinite programming (2020)
- Sun, Defeng; Toh, Kim-Chuan; Yuan, Yancheng; Zhao, Xin-Yuan: SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0) (2020)
- Cafuta, Kristijan: Sums of Hermitian squares decomposition of non-commutative polynomials in non-symmetric variables using NCSOStools (2019)
- Ito, Naoki; Kim, Sunyoung; Kojima, Masakazu; Takeda, Akiko; Toh, Kim-Chuan: Algorithm 996: BBCPOP: a sparse doubly nonnegative relaxation of polynomial optimization problems with binary, box, and complementarity constraints (2019)