Convex Optimization

Mathematica 12: Convex Optimization

Version 12 expands the scope of optimization solvers in the Wolfram Language to include optimization of convex functions over convex constraints. Convex optimization is a class of problems for which there are fast and robust optimization algorithms, both in theory and in practice. Just as advances in linear optimization opened up many industrial applications, ever-wider classes of problems are being identified to be convex in a wide variety of domains, such as statistics, finance, signal processing, geometry and many more.

Mathematica 12: Convex Optimization
  • New set of functions for classes of convex optimization.
  • Enhanced support for linear optimization. »
  • Support for linear fractional optimization. »
  • Support for quadratic optimization. »
  • Support for second-order cone optimization. »
  • Support for semidefinite optimization. »
  • Support for conic optimization. »
  • Support for primal and dual solution properties.
  • Support for matrix- or formula-based modeling input.
  • Vector inequalities for modeling with vector-valued variables.
  • Automatic dimensional inference for vector-valued variables.
  • Automatic detection of convex problems in existing general optimization functions.
  • Automatic use of convex optimization for specific tasks.

Related Examples