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️ help us benchmark cvxpy solvers contribute problems and results to the solver benchmarks project read our new paper on the cvxpy internals welcome to cvxpy 1 9 initializing search cvxpy install user guide functions api documentation examples contributing changelog faq resources cvxpy workshop cvxpy install user guide functions api documentation examples contributing changelog faq resources cvxpy workshop welcome to cvxpy 1 9 convex optimization for everyone help us benchmark cvxpy solvers contribute problems and results to the solver benchmarks project we are building a cvxpy community on discord join the conversation cvxpy is an open source python embedded modeling language for convex optimization problems it lets you express your problem in a natural way that follows the math rather than in the restrictive standard form required by solvers for example the following code solves a least squares problem with box constraints import cvxpy as cp import numpy as np problem data m 30 n 20 np random seed 1 a np random randn m n b np random randn m construct the problem x cp variable n objective cp minimize cp sum_squares a x b constraints 0 x x 1 prob cp problem objective constraints the optimal objective value is returned by prob solve result prob solve the optimal value for x is stored in x value print x value the optimal lagrange multiplier for a constraint is stored in constraint dual_value print constraints 0 dual_value this short script is a basic example of what cvxpy can do in addition to convex programming cvxpy also supports parametrized programming geometric programming mixed integer convex programs quasiconvex programming and nonlinear programming for a guided tour of cvxpy check out the tutorial for applications to machine learning control finance and more browse the library of examples for applications that involve nonlinear programming visit the library of dnlp examples for background on convex optimization see the book convex optimization by boyd and vandenberghe cvxpy relies on the open source solvers clarabel osqp scs highs additional solvers are supported but must be installed separately community the cvxpy community consists of researchers data scientists software engineers and students from all over the world we welcome you to join us to chat with the cvxpy community in real time join us on discord to have longer in depth discussions with the cvxpy community use github discussions to share feature requests and bug reports use the issue tracker development cvxpy is a community project built from the contributions of many researchers and engineers cvxpy is developed and maintained by steven diamond riley murray philipp schiele parth nobel and william zhang cvxpy s emeritus maintainers are bartolomeo stellato and akshay agrawal many others have contributed significantly a non exhaustive list of people who have shaped cvxpy over the years includes stephen boyd eric chu robin verschueren jaehyun park enzo busseti aj friend judson wilson chris dembia and daniel cederberg we appreciate all contributions to get involved see our contributing guide and join us on discord news cvxpy 1 9 introduces disciplined nonlinear programming dnlp a grammar for specifying nonlinear programs nlp which extends cvxpy beyond convex optimization cvxpy currently supports four nlp solvers ipopt knitro uno and copt for a complete list of changes see the changelog back to top copyright the cvxpy authors created using sphinx 8 2 3 and sphinx immaterial