# "Gurobi"(Optimization Method)

• "Gurobi" calls the Gurobi optimization solver library.

# Details

• 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 for Gurobi.
• Method"Gurobi" can be used in any convex optimization function as well as with NMinimize and related functions for appropriate problems.
• Possible options for method "Gurobi" and their corresponding default values are:
•  MaxIterations Automatic maximum number of iterations to use Tolerance Automatic the tolerance to use for internal comparison

# Examples

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## Basic Examples(2)

Minimize subject to the constraint with method "Gurobi":

Minimize subject to the constraints , for integer with method "Gurobi":

## Scope(12)

### Applicable Functions(6)

Use NMaximize with method "Gurobi" to maximize subject to linear constraints:

Use ConvexOptimization with method "Gurobi" to minimize 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 :

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(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 10,000-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: