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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


Source Files

Data Files





folioqp_graph.mos

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

   file folioqp_graph.mos
   ``````````````````````
   Modeling a small QP problem 
   to perform portfolio optimization.
   Minimize variance subject to different target return.
   Graphical output.
   
  (c) 2008 Fair Isaac Corporation
      author: S.Heipcke, Aug. 2003, rev. Sep. 2017
*******************************************************!)

model "Portfolio optimization with QP, graphical output"
 uses "mmxprs", "mmnl", "mmsvg"      ! Use Xpress Optimizer with QP solver

 parameters
  MAXVAL = 0.3                       ! Max. investment per share
  MINAM = 0.5                        ! Min. investment into N.-American values
 end-parameters

 declarations
  SHARES = 1..10                     ! Set of shares
  RISK: set of integer               ! Set of high-risk values among shares
  NA: set of integer                 ! Set of shares issued in N.-America
  RET: array(SHARES) of real         ! Estimated return in investment
  VAR: array(SHARES,SHARES) of real  ! Variance/covariance matrix of
                                     ! estimated returns
  SOLRET: array(range) of real       ! Solution values (total return)
  SOLDEV: array(range) of real       ! Solution values (average deviation)
 end-declarations

 initializations from "folioqp.dat"
  RISK RET NA VAR
 end-initializations

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

! Objective: mean variance
 Variance:= sum(s,t in SHARES) VAR(s,t)*frac(s)*frac(t) 

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

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

! Solve the problem for a range of returns: this is the efficient frontier
 target:= min(s in SHARES) RET(s)
 RMAX:= max(s in SHARES) RET(s)

 while(target < RMAX) do
   Return:= sum(s in SHARES) RET(s)*frac(s) >= target    ! Target yield
   minimize(Variance)                ! Solve the problem
   
   if (getprobstat = XPRS_OPT) then  ! Save the optimal solution value
    ct+=1
    SOLDEV(ct):= getobjval
    SOLRET(ct):= target
   else
    writeln("No solution for target return >= ", ct, "%")
    break
   end-if
   target += 1
 end-do

! Drawing a graph to represent results (`GrS') and data (`GrL' & `GrH')
 declarations
  DEV: array(SHARES) of real         ! Standard deviation
  NAMES: array(SHARES) of string     ! Names of shares
 end-declarations

 initializations from "folioqpgraph.dat"
  DEV NAMES
 end-initializations
	
 svgaddgroup("GrS", "Solution values", SVG_GREY)
 svgaddgroup("GrL", "Low risk", SVG_GREEN)
 svgaddgroup("GrH", "High risk", SVG_RED)
 
 forall(r in 1..ct) svgaddpoint("GrS", SOLRET(r), SOLDEV(r));

 svgaddline("GrS", sum(r in 1..ct) [SOLRET(r), SOLDEV(r)])
    
 forall (s in SHARES-RISK) do
  svgaddpoint("GrL", RET(s), DEV(s))
  svgaddtext("GrL", RET(s)+1, 1.3*(DEV(s)-1), NAMES(s))
 end-do

 forall (s in RISK) do
  svgaddpoint("GrH", RET(s), DEV(s))
  svgaddtext("GrH", RET(s)-2.5, DEV(s)-1, NAMES(s))
 end-do

! Scale the size of the displayed graph
 svgsetgraphscale(10)
 svgsetgraphpointsize(2)
 svgsetgraphlabels("Expected return", "Standard deviation")

! Optionally save graphic to file
 svgsave("folioqpgraph.svg")

! Display the graph and wait for window to be closed by the user
 svgrefresh
 svgwaitclose("Close browser window to terminate model execution.", 1)

end-model 

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