"Xpress" (Optimization Method)

Details

  • TemplateBox[{Xpress, {URL[https://www.fico.com/en/products/fico-xpress-solver], None}, http://gurobi.com, HyperlinkActionRecycled, {HyperlinkActive}, BaseStyle -> {Hyperlink}, HyperlinkAction -> Recycled}, HyperlinkTemplate] is a commercial optimization solver for linear, quadratic, quadratically constrained quadratic and second-order cone problems with real and mixed-integer variables.
  • View the workflow page for information on how to get a license for Xpress.
  • Method"Xpress" can be used in any convex optimization function as well as with NMinimize and related functions for appropriate problems.
  • Possible options for method "Xpress" and their corresponding default values are:
  • MaxIterationsAutomaticmaximum number of iterations to use
    ToleranceAutomaticthe tolerance to use for internal comparison

Examples

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Basic Examples  (2)

Minimize subject to the constraint with method "Xpress":

Minimize TemplateBox[{{{, {x, ,, y}, }}}, Norm] subject to the constraints , for integer with method "Xpress":

Scope  (11)

Applicable Functions  (6)

Use NMaximize with method "Xpress" to maximize 1-TemplateBox[{{x, +, {2, y}}}, Abs] subject to linear constraints:

Use ConvexOptimization with method "Xpress" to minimize TemplateBox[{{{, {x, ,, {2, , y}}, }}}, Norm] subject to :

Get the minimum value and the minimizing vector using solution properties:

Use ConicOptimization with method "Xpress" to minimize subject to :

Use SecondOrderConeOptimization to minimize subject to :

Define the objective as and the constraints as TemplateBox[{{{{a, _, i}, ., x}, +, {b, _, i}}}, Norm]<=alpha_i.x+beta_i,i=1,2:

Solve using matrix-vector inputs:

Use QuadraticOptimization to minimize subject to and :

Define the objective as and constraints as and :

Solve using matrix-vector inputs:

Use LinearOptimization to minimize subject to :

Combine the coefficients into and use a vector variable :

Scalable Problems  (5)

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 :

Minimize the sum of the integer-valued coordinates of a point lying within a 1,000-dimensional unit ball:

Minimize for a symmetric semidefinite matrix , subject to constraint :