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"Xpress" (Optimization Method)

  • "Xpress" calls the Xpress optimization solver library.

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)Summary of the most common use cases

Minimize subject to the constraint with method "Xpress":

In[30]:=30
✖
https://wolfram.com/xid/0naq8rw770m-bqp9v7
Out[30]=30

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

In[1]:=1
✖
https://wolfram.com/xid/0naq8rw770m-hcp4np
Out[1]=1

Scope  (11)Survey of the scope of standard use cases

Applicable Functions  (6)

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

In[2]:=2
✖
https://wolfram.com/xid/0naq8rw770m-hb4xwg
Out[2]=2

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

In[1]:=1
✖
https://wolfram.com/xid/0naq8rw770m-k10fea
Out[1]=1

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

In[2]:=2
✖
https://wolfram.com/xid/0naq8rw770m-huf1m0
Out[2]=2

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

In[1]:=1
✖
https://wolfram.com/xid/0naq8rw770m-kcro5h
Out[1]=1

Use SecondOrderConeOptimization to minimize subject to :

In[1]:=1
✖
https://wolfram.com/xid/0naq8rw770m-jfdvd7
Out[1]=1

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

In[2]:=2
✖
https://wolfram.com/xid/0naq8rw770m-zamil

Solve using matrix-vector inputs:

In[3]:=3
✖
https://wolfram.com/xid/0naq8rw770m-ind1ps
Out[3]=3

Use QuadraticOptimization to minimize subject to and :

In[1]:=1
✖
https://wolfram.com/xid/0naq8rw770m-zwo3ll
Out[1]=1

Define the objective as and constraints as and :

In[2]:=2
✖
https://wolfram.com/xid/0naq8rw770m-0rc1gi

Solve using matrix-vector inputs:

In[3]:=3
✖
https://wolfram.com/xid/0naq8rw770m-1l2kf5
Out[3]=3

Use LinearOptimization to minimize subject to :

In[1]:=1
✖
https://wolfram.com/xid/0naq8rw770m-b274a8
Out[1]=1

Combine the coefficients into and use a vector variable :

In[2]:=2
✖
https://wolfram.com/xid/0naq8rw770m-lk61me
Out[2]=2

Scalable Problems  (5)

Minimize Total[x] subject to the constraint using vector variable with non-negative values:

In[4]:=4
✖
https://wolfram.com/xid/0naq8rw770m-dgougr
In[8]:=8
✖
https://wolfram.com/xid/0naq8rw770m-fl0xk
Out[8]=8

Minimize Total[x] subject to the constraint with a non-negative integer-valued vector:

In[1]:=1
✖
https://wolfram.com/xid/0naq8rw770m-lmnkmy
In[2]:=2
✖
https://wolfram.com/xid/0naq8rw770m-t2in7
Out[2]=2

Minimize Total[x] subject to the constraint using a vector variable :

In[1]:=1
✖
https://wolfram.com/xid/0naq8rw770m-caacq8
In[2]:=2
✖
https://wolfram.com/xid/0naq8rw770m-dkv8p7
Out[2]=2

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

In[1]:=1
✖
https://wolfram.com/xid/0naq8rw770m-ijsdc1
Out[1]=1

Minimize for a symmetric semidefinite matrix , subject to constraint :

In[1]:=1
✖
https://wolfram.com/xid/0naq8rw770m-byfxxx
In[2]:=2
✖
https://wolfram.com/xid/0naq8rw770m-bubr0j
Out[2]=2