"GUROBI" （机器学习方法）

Details

  • TemplateBox[{GUROBI, {URL[http://gurobi.com], 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.
  • Visit the following page for information on how to get a license from GUROBI.
  • Method"GUROBI" can be used in any convex optimization function as well as NMinimize and related functions for appropriate problems.
  • Possible options for method "GUROBI" and their corresponding default values are:
  • MaxIterationsAutomaticmaximum number of iterations to use
    ToleranceAutomaticthe tolerance to use for internal comparison

范例

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基本范例  (2)

Minimize subject to the constraint with method "GUROBI":

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

Scope  (12)

Applicable Functions  (6)

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

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

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

Use ConicOptimization with method "GUROBI" 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 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  (6)

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 10000-dimensional unit ball:

Minimize for a symmetric semidefinite matrix , subject to constraint :

Minimize x.Q.x+Total[x], for a sparse symmetric semidefinite matrix , subject to Total[x]≥1: