This software is designed to solve conic programming problems whose constraint cone is a product of semidefinite cones, second-order cones, nonnegative orthants and Euclidean spaces; and whose objective function is the sum of linear functions and log-barrier terms associated with the constraint cones. This includes the special case of determinant maximization problems with linear matrix inequalities. It employs an infeasible primal-dual predictor-corrector path-following method, with either the HKM or the NT search direction. The basic code is written in Matlab, but key subroutines in C are incorporated via Mex files. Routines are provided to read in problems in either SDPA or SeDuMi format. Sparsity and block diagonal structure are exploited. We also exploit low-rank structures in the constraint matrices associated the semidefinite blocks if such structures are explicitly given. To help the users in using our software, we also include some examples to illustrate the coding of problem data for our SQLP solver. Various techniques to improve the efficiency and stability of the algorithm are incorporated. For example, step-lengths associated with semidefinite cones are calculated via the Lanczos method. Numerical experiments show that this general purpose code can solve more than 80% of a total of about 300 test problems to an accuracy of at least 10−6 in relative duality gap and infeasibilities.

References in zbMATH (referenced in 644 articles , 1 standard article )

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  1. Canelas, Alfredo; Carrasco, Miguel; López, Julio: A feasible direction algorithm for nonlinear second-order cone programs (2019)
  2. Crespo, Luis G.; Colbert, Brendon K.; Kenny, Sean P.; Giesy, Daniel P.: On the quantification of aleatory and epistemic uncertainty using sliced-normal distributions (2019)
  3. Cui, Yiran; Morikuni, Keiichi; Tsuchiya, Takashi; Hayami, Ken: Implementation of interior-point methods for LP based on Krylov subspace iterative solvers with inner-iteration preconditioning (2019)
  4. de Oliveira, Fúlvia S. S.; Souza, Fernando O.: Strong delay-independent stability of linear delay systems (2019)
  5. Faybusovich, Leonid; Zhou, Cunlu: Long-step path-following algorithm for solving symmetric programming problems with nonlinear objective functions (2019)
  6. Frot, Benjamin; Jostins, Luke; McVean, Gilean: Graphical model selection for Gaussian conditional random fields in the presence of latent variables (2019)
  7. Hippert, Benjamin; Uhde, André; Wengerek, Sascha Tobias: Portfolio benefits of adding corporate credit default swap indices: evidence from North America and Europe (2019)
  8. Hu, Shenglong: An elementary proof for the exact relaxation for rank one moment matrices in multi-polynomial SOS relaxation (2019)
  9. Hu, Shenglong; Sun, Defeng; Toh, Kim-Chuan: Best nonnegative rank-one approximations of tensors (2019)
  10. 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)
  11. Jafari, Saeid; Ioannou, Petros A.: Reference tracking control and attenuation of unknown periodic disturbances in the presence of noise for unknown minimum-phase LTI plants (2019)
  12. Jaoude, Dany Abou; Farhood, Mazen: Coprime factors reduction of distributed nonstationary LPV systems (2019)
  13. Kian, Ramez; Berk, Emre; Gürler, Ülkü: Minimal conic quadratic reformulations and an optimization model (2019)
  14. Kimizuka, Masaki; Kim, Sunyoung; Yamashita, Makoto: Solving pooling problems with time discretization by LP and SOCP relaxations and rescheduling methods (2019)
  15. King, Emily J.; Tang, Xiaoxian: New upper bounds for equiangular lines by pillar decomposition (2019)
  16. Komeiji, Hikaru; Kim, Sunyoung; Yamashita, Makoto: On the conditions for the finite termination of ADMM and its applications to SOS polynomials feasibility problems (2019)
  17. Laiu, M. Paul; Tits, André L.: A constraint-reduced MPC algorithm for convex quadratic programming, with a modified active set identification scheme (2019)
  18. Lee, Jae Hyoung; Jiao, Liguo: Finding efficient solutions for multicriteria optimization problems with SOS-convex polynomials (2019)
  19. Li, Lu; Wang, Lun; Wang, Guoqiang; Li, Na; Zhang, Juli: Linearized alternating direction method of multipliers for separable convex optimization of real functions in complex domain (2019)
  20. Liu, Yanli; Ryu, Ernest K.; Yin, Wotao: A new use of Douglas-Rachford splitting for identifying infeasible, unbounded, and pathological conic programs (2019)

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