SeDuMi

SeDuMi is a Matlab toolbox for solving optimization problems over symmetric cones, i.e. it allows not only for linear constraints, but also quasiconvex-quadratic constraints and positive semi-definiteness constraints. Complex valued entries are allowed. Features include symbolic and numerical reordering schemes, balancing speed/accuracy performance and sophisticated dense column handling, using the Goldfarb-Scheinberg product form idea.


References in zbMATH (referenced in 760 articles , 2 standard articles )

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  1. Amir Ali Ahmadi, Anirudha Majumdar: DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization (2017) arXiv
  2. Arima, Naohiko; Kim, Sunyoung; Kojima, Masakazu; Toh, Kim-Chuan: A robust Lagrangian-DNN method for a class of quadratic optimization problems (2017)
  3. Canelas, Alfredo; Carrasco, Miguel; López, Julio: Application of the sequential parametric convex approximation method to the design of robust trusses (2017)
  4. Claeys, Mathieu; Henrion, Didier; Kružík, Martin: Semi-definite relaxations for optimal control problems with oscillation and concentration effects (2017)
  5. Dumitrescu, Bogdan: Positive trigonometric polynomials and signal processing applications (2017)
  6. Fan, Jinyan; Zhou, Anwa: A semidefinite algorithm for completely positive tensor decomposition (2017)
  7. Harman, Radoslav; Benková, Eva: Barycentric algorithm for computing D-optimal size- and cost-constrained designs of experiments (2017)
  8. Huang, Kuo-Ling; Mehrotra, Sanjay: Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method (2017)
  9. Korda, Milan; Jones, Colin N.: Stability and performance verification of optimization-based controllers (2017)
  10. Lu, Cheng; Deng, Zhibin; Jin, Qingwei: An eigenvalue decomposition based branch-and-bound algorithm for nonconvex quadratic programming problems with convex quadratic constraints (2017)
  11. Mohammad-Nezhad, Ali; Terlaky, Tamás: A polynomial primal-dual affine scaling algorithm for symmetric conic optimization (2017)
  12. Nie, Jiawang; Wang, Li; Ye, Jane J.: Bilevel polynomial programs and semidefinite relaxation methods (2017)
  13. Papp, Dávid: Semi-infinite programming using high-degree polynomial interpolants and semidefinite programming (2017)
  14. Permenter, Frank; Friberg, Henrik A.; Andersen, Erling D.: Solving conic optimization problems via self-dual embedding and facial reduction: A unified approach (2017)
  15. Pirhaji, M.; Mansouri, H.; Zangiabadi, M.: An $O(\sqrt nL)$ wide neighborhood interior-point algorithm for semidefinite optimization (2017)
  16. Sakaue, Shinsaku; Takeda, Akiko; Kim, Sunyoung; Ito, Naoki: Exact semidefinite programming relaxations with truncated moment matrix for binary polynomial optimization problems (2017)
  17. Salahi, Maziar; Taati, Akram; Wolkowicz, Henry: Local nonglobal minima for solving large-scale extended trust-region subproblems (2017)
  18. Taylor, Adrien B.; Hendrickx, Julien M.; Glineur, François: Smooth strongly convex interpolation and exact worst-case performance of first-order methods (2017)
  19. Taylor, Adrien B.; Hendrickx, Julien M.; Glineur, François: Exact worst-case performance of first-order methods for composite convex optimization (2017)
  20. Wang, Ximing; Fan, Neng; Pardalos, Panos M.: Stochastic subgradient descent method for large-scale robust chance-constrained support vector machines (2017)

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