cvxpy/cvxpy

Features

• Disciplined Convex Programming Compilers - Forms the core compilation engine that translates user expressions into solver-ready canonical form.
• Embedded Domain-Specific Languages - A Python-embedded domain-specific language for modeling and solving convex optimization problems using natural syntax.
• Modeling Frameworks - Provides a domain-specific language for modeling convex optimization problems in natural mathematical syntax.
• Implicit Differentiation Engines - Provides sensitivity analysis by computing derivatives of optimal solutions with respect to problem parameters.
• Convex Optimization Solvers - Provides a Python-embedded DSL for formulating and solving convex optimization problems using natural syntax.
• Disciplined Convex Programming Frameworks - Automatically enforces convexity rules through a disciplined convex programming framework.
• Mathematical Optimization Solving - Finds the optimal value of an objective function subject to defined constraints and variables.
• Optimization Expression Functions - Provides atomic mathematical functions like exp, log, and sqrt for use within optimization expressions.
• Optimization Variable Declarations - Declares symbolic variables that represent unknowns in convex optimization problems.
• Symbolic Parameter Declarations - Declares symbolic parameters whose values can be changed without redefining the entire optimization problem.
• Optimization Constraint Enforcement - Adds equality, inequality, and other constraints to restrict the feasible solution space.
• Solver-Agnostic Optimization Interfaces - Abstracts away backend details and dispatches problems to specialized solvers like ECOS, SCS, and Gurobi.
• Optimal Value Retrieval - Retrieves computed optimal variable values and Lagrange multipliers after solving optimization problems.
• Solver Dispatchers - Automatically selects the most specialized solver for a problem type or allows manual override.
• Convex Optimization Formulations - Ships built-in formulations for ridge, lasso, logistic regression, and SVM as convex optimization problems.
• Convex Model Fitting - Fits regression and classification models like ridge, lasso, logistic, and SVM as convex optimization problems.
• Quasiconvex Optimization Solvers - Handles quasiconvex objectives and constraints by transforming them into sequences of convex subproblems.
• Sensitivity Analysis - Computes derivatives of optimal solutions with respect to problem parameters for sensitivity analysis.
• Portfolio Optimization - Constructs and solves mean-variance, entropic, and risk-based portfolio allocation problems as convex programs.
• Geometric Program Formulations - Formulates and solves geometric programs using declarative syntax that converts non-convex forms to convex ones.
• Geometric Program Log-Transformations - Transforms geometric programs into convex form using logarithmic variable changes for solver compatibility.
• Mathematical Solution Derivation - Computes derivatives of optimal solutions with respect to problem parameters for sensitivity analysis.
• Mixed-Integer Convex Solvers - Supports integer and binary variables alongside convex objectives and constraints for combinatorial problems.
• Mixed-Integer Canonicalization Engines - Transforms mixed-integer problems into forms accepted by solvers while preserving convex structure.
• Geometric Programming Tools - Formulates and solves geometric programs using disciplined rules that convert non-convex forms to convex ones.
• Solver Option Configurations - Pass solver-specific parameters like iteration limits and accuracy tolerances through keyword arguments.
• Nonlinear Optimization Solvers - Formulates and solves nonlinear optimization problems using disciplined nonlinear programming techniques.
• Disciplined Grammar Frameworks - Extends convex optimization to nonlinear programming using a disciplined grammar for expression validation.
• Disciplined Nonlinear Programming Grammars - Extends convex optimization to nonlinear programs using a disciplined grammar with solvers like IPOPT and KNITRO.
• Ordinary Least Squares - Minimizes the sum of squared residuals subject to box constraints by constructing a convex objective.
• Quasiconvex Optimization Solvers - Solves quasiconvex optimization problems by transforming them into sequences of convex subproblems.
• Quasiconvex Program Solvers - Supports quasiconvex objectives and constraints by transforming them into sequences of convex subproblems.
• Quasiconvex Bisection Engines - Solves quasiconvex programs by iterating convex feasibility subproblems with bisection search.
• Custom Solver Integrations - Add a user-defined solver by subclassing the QP or conic solver interface.
• Data Science - Modeling language for convex optimization.
• Data Science and Databases - Modeling language for convex optimization problems.
• Scientific Computing Libraries - Modeling language for convex optimization problems.

Star history