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|[[MATLAB]]
|Interfaces with SeDuMi and SDPT3 solvers; designed to only express convex optimization problems.
|<ref name=":3">{{Cite web|last=Borchers|first=Brian|title=An Overview Of Software For Convex Optimization|url=http://infohost.nmt.edu/~borchers/presentation.pdf|url-status=dead|archive-url=https://web.archive.org/web/20170918180026/http://infohost.nmt.edu/~borchers/presentation.pdf|archive-date=2017-09-18|access-date=12 Apr 2021}}</ref>
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|[[Python (programming language)|Python]]
|Interfaces with the [https://cvxopt.org/ CVXOPT] solver.
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|[[Julia (programming language)|Julia]]
|Disciplined convex programming, supports many solvers.
|<ref>{{cite arXiv |last1=Udell |first1=Madeleine |last2=Mohan |first2=Karanveer |last3=Zeng |first3=David |last4=Hong |first4=Jenny |last5=Diamond |first5=Steven |last6=Boyd |first6=Stephen |date=2014-10-17 |title=Convex Optimization in Julia |class=math.OC |eprint=1410.4821 }}</ref>
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|[[R (programming language)|R]]
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|<ref>{{Cite web|title=Disciplined Convex Optimiation - CVXR|url=https://www.cvxgrp.org/CVXR/|access-date=2021-06-17|website=www.cvxgrp.org}}</ref>
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|MATLAB, Octave
|Interfaces with CPLEX, GUROBI, MOSEK, SDPT3, SEDUMI, CSDP, SDPA, PENNON solvers; also supports integer and nonlinear optimization, and some nonconvex optimization. Can perform [[robust optimization]] with uncertainty in LP/SOCP/SDP constraints.
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|MATLAB
|Expresses and solves semidefinite programming problems (called "linear matrix inequalities")
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|Transforms LMI lab problems into SDP problems.
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|MATLAB
|Similar to LMI lab, but uses the SeDuMi solver.
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|Can do robust optimization on linear programming (with MOSEK to solve second-order cone programming) and [[mixed integer linear programming]]. Modeling package for LP + SDP and robust versions.
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|Modeling system for robust optimization. Supports distributionally robust optimization and [[uncertainty set]]s.
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Octave
|Modeling system for polynomial optimization.
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|Modeling system for [[polynomial optimization]]. Uses SDPT3 and SeDuMi. Requires Symbolic Computation Toolbox.
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|Modeling system for polynomial optimization. Uses the SDPA or SeDuMi solvers.
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|Supports primal-dual methods for LP + SOCP. Can solve LP, QP, SOCP, and mixed integer linear programming problems.
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|[[C (programming language)|C]]
|Supports primal-dual methods for LP + SDP. Interfaces available for MATLAB, [[R (programming language)|R]], and Python. Parallel version available. SDP solver.
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|Python
|Supports primal-dual methods for LP + SOCP + SDP. Uses Nesterov-Todd scaling. Interfaces to MOSEK and DSDP.
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|Supports primal-dual methods for LP + SOCP.
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|MATLAB, Octave, [[MEX file|MEX]]
|Solves LP + SOCP + SDP. Supports primal-dual methods for LP + SOCP + SDP.
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|[[C++]]
|Solves LP + SDP. Supports primal-dual methods for LP + SDP. Parallelized and extended precision versions are available.
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|MATLAB, Octave, MEX
|Solves LP + SOCP + SDP. Supports primal-dual methods for LP + SOCP + SDP.
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|Supports general-purpose codes for LP + SOCP + SDP. Uses a bundle method. Special support for SDP and SOCP constraints.
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|Supports general-purpose codes for LP + SDP. Uses a dual interior point method.
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|Supports general-purpose codes for SOCP, which it treats as a nonlinear programming problem.
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|Supports general-purpose codes. Uses an augmented Lagrangian method, especially for problems with SDP constraints.
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|Supports general-purpose codes. Uses low-rank factorization with an augmented Lagrangian method.
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|Modeling system for linear, nonlinear, mixed integer linear/nonlinear, and second-order cone programming problems.
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