"MOSEK" (Optimization Method)
- MOSEK is a commercial solver for large-scale sparse linear and quadratic optimization problems with real and mixed-integer variables and conic optimization problems with real variables.
- In addition to real-valued conic problems, MOSEK allows mixed-integer variables in combination with the linear, quadratic, exponential and power cones.
- Visit the following page for information on how to get a license from MOSEK ApS.
- Method"MOSEK" can be used in any convex optimization function as well as NMinimize and related functions for appropriate problems.
- Possible options for method "MOSEK" and their corresponding default values are:
MaxIterations Automatic maximum number of iterations to use Tolerance Automatic the tolerance to use for internal comparisons Method Automatic MOSEK submethod
Examplesopen allclose all
Basic Examples (2)
Applicable Functions (8)
Use NMaximize with method "MOSEK" to maximize subject to linear constraints:
Use ConvexOptimization to minimize over a disk centered at with radius
Use ConicOptimization to minimize subject to and :
Use SemidefiniteOptimization to minimize subject to the positive semidefinite matrix constraint :
Use SecondOrderConeOptimization to minimize subject to :
Use QuadraticOptimization to minimize minimize subject to and :
Use LinearOptimization to minimize subject to :
Use GeometricOptimization to maximize the area of a rectangle such that the perimeter is at most 1:
Scalable Problems (9)
Minimize Total[x] subject to the constraint using vector variable with non-negative values:
Minimize Total[x] subject to the constraint with a non-negative integer valued vector:
Minimize Total[x] subject to the constraint using a vector variable :