The definitive Wolfram Language and notebook experience
The original technical computing environment
All-in-one AI assistance for your Wolfram experience
We deliver solutions for the AI eraâcombining symbolic computation, data-driven insights and deep technology expertise.
Courses in computing, science, life and more
Learn, solve problems and share ideas.
News, views and insights from Wolfram
Resources for
Software DevelopersWe deliver solutions for the AI eraâcombining symbolic computation, data-driven insights and deep technology expertise.
Wolfram SolutionsCourses in computing, science, life and more
Learn, solve problems and share ideas.
News, views and insights from Wolfram
Resources for
Software DevelopersConvex optimization is the problem of minimizing a convex function over convex constraints. It is a class of problems for which there are fast and robust optimization algorithms, both in theory and in practice. Following the pattern for linear optimization, ever-wider classes of problems are being identified to be in this class in a wide variety of domains, such as statistics, finance, signal processing, geometry and many more. The new classification of optimization problems is now convex and nonconvex optimization. The Wolfram Language provides the major convex optimization classes, their duals and sensitivity to constraint perturbation. The classes are extensively exemplified and should also provide a learning tool. The general optimization functions automatically recognize and transform a wide variety of problems into these optimization classes. Problem constraints can be compactly modeled using vector variables and vector inequalities.
ConvexOptimization — minimize with convex
ParametricConvexOptimization — minimize with parameters
RobustConvexOptimization — minimize with uncertainties
Convex Optimization ClassesLinearOptimization — minimize
LinearFractionalOptimization — minimize
QuadraticOptimization — minimize
SecondOrderConeOptimization — minimize
SemidefiniteOptimization — minimize
GeometricOptimization — minimize
ConicOptimization — minimize
Vector Inequality ConstraintsVectorGreaterEqual — partial ordering for vectors and matrices
VectorLessEqual ▪ VectorGreater ▪ VectorLess
General Convex & Nonconvex Optimization »FindMinimum — numerical local constrained optimization
FindMaximum ▪ FindMinValue ▪ FindMaxValue ▪ FindArgMin ▪ FindArgMax
NMinimize — numerical global constrained optimization
NMaximize ▪ NMinValue ▪ NMaxValue ▪ NArgMin ▪ NArgMax
Minimize — symbolic global constrained optimization
Maximize ▪ MinValue ▪ MaxValue ▪ ArgMin ▪ ArgMax
Commercial SolversMOSEK — conic optimization solvers from MOSEK ApS
Gurobi — quadratic and linear optimization solvers from Gurobi
Xpress — quadratic and linear optimization solvers from FICO Xpress
Related Tech Notes Related GuidesRetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4