FICO Xpress Optimization Examples Repository
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Introductory examples

Problem name and type, featuresDifficulty
approx Approximation: Piecewise linear approximation **
SOS-2, Special Ordered Sets, piecewise linear approximation of a nonlinear function
burglar MIP modeling: Knapsack problem: 'Burglar' *
simple MIP model with binary variables, data input from text data file, array initialization, numerical indices, string indices, record data structure
chess LP modeling: Production planning: 'Chess' problem *
simple LP model, solution output, primal solution values, slack values, activity values, dual solution values
pricebrai All item discount pricing: Piecewise linear function ***
SOS-1, Special Ordered Sets, piecewise linear function, approximation of non-continuous function, step function
pricebrinc Incremental pricebreaks: Piecewise linear function ***
SOS-2, Special Ordered Sets, piecewise linear function, step function

Further explanation of this example: 'Applications of optimization with Xpress-MP', Introductory examples (Chapters 1 to 5) of the book 'Applications of optimization with Xpress-MP'[download all files]

Source Files

Data Files


   Mosel Example Problems

   file chess.mos
   Production of chess boards   

   (c) 2008 Fair Isaac Corporation
       author: R.C. Daniel, Jul. 2002

model Chess
 uses "mmxprs"
  xs, xl: mpvar                   ! Decision variables: produced quantities

 Profit:=  5*xs + 20*xl           ! Objective function
 Boxwood:= 1*xs + 3*xl <=  200    ! kg of boxwood
 Lathe:=   3*xs + 2*xl <=  160    ! Lathehours
 maximize(Profit)                 ! Solve the problem

 writeln("LP Solution:")          ! Solution printing
 writeln(" Objective: ", getobjval)
 writeln("Make ", getsol(xs), " small sets")
 writeln("Make ", getsol(xl), " large sets")

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