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Folio - Modelling examples from 'Getting started'

Description
  • Chapter 3 Inputting and Solving a Linear Programming problem
    • foliolp.mos: modeling and solving a small LP problem
    • foliolperr.mos: LP model with syntax errors
    • foliolps.mos: LP model using string indices
  • Chapter 4 Working with data
    • foliodata.mos (data file: folio.dat): data input from file, result output to a file, model parameters
    • folioodbc.mos (data files: folio.xls, folio.mdb, folio.sqlite): data input from a spreadsheet or database, result output to a spreadsheet or database, model parameters
    • folioexcel.mos (data file: folio.xls): same as folioodbc.mos but with Excel-specific data input and output (Windows only)
    • foliosheet.mos (data file: folio.xls): same as folioodbc.mos but with data input and output through generic spreadsheet access
    • foliocsv.mos (data file: folio.csv): same as folioodbc.mos but with data input and output through generic spreadsheet access in CSV format
  • Chapter 5 Drawing user graphs
    • folioloop.mos (data files: folio.dat, foliodev.dat): re-solving with varied parameter settings
    • folioloop_graph.mos (data files: folio.dat, foliodev.dat): re-solving with varied parameter settings, graphical solution display
    • foliolps_graph.mos: same as foliolps, adding graphical solution display
  • Chapter 6 Mixed Integer Programming
    • foliomip1.mos (data file: folio.dat): modeling and solving a small MIP problem (binary variables)
    • foliomip2.mos (data file: folio.dat): modeling and solving a small MIP problem (semi-continuous variables)
  • Chapter 7 Quadratic Programming
    • folioqp.mos (data file: folioqp.dat): modeling and solving a QP and a MIQP problem
    • folioqp_graph.mos (data files: folioqp.dat, folioqpgraph.dat): re-solving a QP problem with varied parameter settings, graphical solution display
    • folioqc.mos (data file: folioqp.dat): modeling and solving a QCQP and
    • foliomiqc.mos (data file: folioqp.dat): modeling and solving a MIQCQP
  • Chapter 8 Heuristics
    • folioheur.mos (data file: folio.dat): heuristic solution of a MIP problem

folio.zip[download all files]

Source Files
By clicking on a file name, a preview is opened at the bottom of this page.
foliolp.mos[download]
foliolperr.mos[download]
foliolps.mos[download]
foliolps_graph.mos[download]
foliodata.mos[download]
folioodbc.mos[download]
folioexcel.mos[download]
foliosheet.mos[download]
foliocsv.mos[download]
folioloop.mos[download]
folioloop_graph.mos[download]
foliomip1.mos[download]
foliomip2.mos[download]
folioqp.mos[download]
folioqp_graph.mos[download]
folioqc.mos[download]
foliomiqc.mos[download]
folioheur.mos[download]
folioqpr.mos[download]
folioqpgraphr.mos[download]

Data Files
folio.dat[download]
folio.csv[download]
foliodev.dat[download]
folioqp.dat[download]
folioqpgraph.dat[download]
folio.xls[download]
folio.mdb[download]
folio.sqlite[download]
folioqphist.dat[download]




foliolps.mos

(!******************************************************
   Mosel Example Problems
   ======================

   file foliolps.mos
   `````````````````
   Modeling a small LP problem 
   to perform portfolio optimization.
   -- Using string indices --
   
  (c) 2008 Fair Isaac Corporation
      author: S.Heipcke, Mar. 2006
*******************************************************!)

model "Portfolio optimization with LP"
 uses "mmxprs"

 declarations
                                     ! Set of shares
  SHARES = {"treasury", "hardware", "theater", "telecom", "brewery", 
            "highways", "cars", "bank", "software", "electronics"}
                                     ! Set of high-risk values among shares
  RISK = {"hardware", "theater", "telecom", "software", "electronics"}
                                    ! Set of shares issued in N.-America
  NA = {"treasury", "hardware", "theater", "telecom"}

  RET: array(SHARES) of real         ! Estimated return in investment

  frac: array(SHARES) of mpvar       ! Fraction of capital used per share
 end-declarations

 RET::(["treasury", "hardware", "theater", "telecom", "brewery", "highways",
       "cars", "bank", "software", "electronics"])[5,17,26,12,8,9,7,6,31,21]
 
! Objective: total return
 Return:= sum(s in SHARES) RET(s)*frac(s) 

! Limit the percentage of high-risk values
 sum(s in RISK) frac(s) <= 1/3

! Minimum amount of North-American values
 sum(s in NA) frac(s) >= 0.5

! Spend all the capital
 sum(s in SHARES) frac(s) = 1
 
! Upper bounds on the investment per share
 forall(s in SHARES) frac(s) <= 0.3

! Solve the problem
 maximize(Return)
 
! Solution printing
 writeln("Total return: ", getobjval)
 forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")  

end-model
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