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. 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 Robust Data Fitting » Data Classification » Optimal Truss Design » Optimal Assignment Problem » Distance between Convex Polyhedra » Trajectory Optimization » Facility Location Problem » Structural Optimization Problem » Eigenvalue Optimization » Max-Cut Problem » Analytic Center » Maximum-Volume Cuboid »
