ECOS

ECOS is an open-source numerical software package for solving optimization problems with second-order cone constraints (SOCPs). This includes linear (LPs), quadratic (QPs), and quadratically-constrained quadratic programs (QCQPs). ECOS also supports a small number of binary or integer variables by employing a simple branch and bound technique. ECOS is written entirely in ANSI C and does not depend on dedicated libraries for the required linear algebra computations operating on the (sparse) problem data. As a consequence, it can be used to solve optimization problems on any embedded system for which a C-compiler is available. The implemented solution algorithm is an interior-point method that is an efficient standard algorithm for solving convex optimization problems. It uses regularization and iterative refinement techniques to be numerically robust. The solution methods have been developed in cooperation with Prof. Stephen Boyd of Stanford University. A number of helpful contributors have provided interfaces to the following programming and modeling languages: CVX (Michael Grant), YALMIP (Johan Löfberg), Julia (João Felipe Santos, Iain Dunning, Anthony Kelman)


References in zbMATH (referenced in 58 articles )

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  1. Kroer, Christian; Peysakhovich, Alexander; Sodomka, Eric; Stier-Moses, Nicolas E.: Computing large market equilibria using abstractions (2022)
  2. Lai, Xin; Bai, Shuliang; Lin, Yong: Normalized discrete Ricci flow used in community detection (2022)
  3. Verschueren, Robin; Frison, Gianluca; Kouzoupis, Dimitris; Frey, Jonathan; van Duijkeren, Niels; Zanelli, Andrea; Novoselnik, Branimir; Albin, Thivaharan; Quirynen, Rien; Diehl, Moritz: \textttacados-- a modular open-source framework for fast embedded optimal control (2022)
  4. Barratt, Shane; Angeris, Guillermo; Boyd, Stephen: Optimal representative sample weighting (2021)
  5. Bouza, Gemayqzel; Quintana, Ernest; Tammer, Christiane: A steepest descent method for set optimization problems with set-valued mappings of finite cardinality (2021)
  6. Dávid Papp, Sercan Yıldız: alfonso: Matlab package for nonsymmetric conic optimization (2021) arXiv
  7. Hirshberg, David A.; Wager, Stefan: Augmented minimax linear estimation (2021)
  8. Kunhippurayil, Sheril; Harris, Matthew W.; Jansson, Olli: Lossless convexification of optimal control problems with annular control constraints (2021)
  9. Mendez-Civieta, Alvaro; Aguilera-Morillo, M. Carmen; Lillo, Rosa E.: Adaptive sparse group LASSO in quantile regression (2021)
  10. Moehle, Nicholas; Kochenderfer, Mykel J.; Boyd, Stephen; Ang, Andrew: Tax-aware portfolio construction via convex optimization (2021)
  11. Murray, Riley; Chandrasekaran, Venkat; Wierman, Adam: Signomial and polynomial optimization via relative entropy and partial dualization (2021)
  12. Murray, Riley; Chandrasekaran, Venkat; Wierman, Adam: Newton polytopes and relative entropy optimization (2021)
  13. Robert Andrew Martin: PyPortfolioOpt: portfolio optimization in Python (2021) not zbMATH
  14. Schwendinger, Florian; Grün, Bettina; Hornik, Kurt: A comparison of optimization solvers for log binomial regression including conic programming (2021)
  15. Tanneau, Mathieu; Anjos, Miguel F.; Lodi, Andrea: Design and implementation of a modular interior-point solver for linear optimization (2021)
  16. Bienstock, Dan; Escobar, Mauro; Gentile, Claudio; Liberti, Leo: Mathematical programming formulations for the alternating current optimal power flow problem (2020)
  17. Capatti, Zeno; Hirschi, Valentin; Kermanschah, Dario; Pelloni, Andrea; Ruijl, Ben: Numerical loop-tree duality: contour deformation and subtraction (2020)
  18. Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
  19. Geng, Zhenglin; Johnson, Daniel; Fedkiw, Ronald: Coercing machine learning to output physically accurate results (2020)
  20. Hütter, Jan-Christian; Mao, Cheng; Rigollet, Philippe; Robeva, Elina: Optimal rates for estimation of two-dimensional totally positive distributions (2020)

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