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Folio - Advanced modelling and solving tasks

Description
Advanced modelling and solving tasks for a portfolio optimization problem:
  • Automated solver tuning (foliolptune.mos)
  • Defining an integer solution callback (foliocb.mos)
  • Using the solution enumerator for multiple MIP solutions (folioenumsol.mos)
  • Handling infeasibility
    • handling infeasibility through deviation variables (folioinfeas.mos)
    • retrieving infeasible row/column from presolve (folioinfcause.mos)
    • retrieving IIS - LP or MIP infeasible (folioiis.mos, foliomiis.mos)
    • using the built-in infeasibility repair functionality (foliorep.mos)
    • same as foliorep, using an 'mpsol' object (foliorep_sol.mos)
  • Data transfer in memory
    • running foliomemio.mos with data transfer in memory (runfolio.mos)
    • same running foliomemio2.mos, grouping tables with identical index sets in "initializations" blocks (runfolio2.mos)
    • master model running several model instances in parallel (runfoliopar.mos)
  • Remote models on a distributed architecture
    • running foliomemio.mos on a remote instance of Mosel (runfoliodistr.mos)
    • master model running several model instances in parallel, each on a different (remote) instance of Mosel (runfoliopardistr.mos)
  • Remote execution via XPRD
    • See examples in the Mosel Whitepapers directory moselpar/XPRD
  • XML and JSON data formats
    • reading data from an XML file, solution output in XML format on screen and to a new file (folioxml.mos, folioxmlqp.mos)
    • generate HTML output file as an XML document (runfolioxml.mos)
    • using JSON-format data files, reading data from a JSON file, solution output in JSON format on screen and to a new file (foliojson.mos)
  • HTTP
    • starting an HTTP server managing requests from HTTP clients (foliohttpsrv.mos)
    • HTTP client exchanging XML data files with an HTTP server (foliohttpclient.mos)


Source Files

Data Files





foliomip3.mos

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

   file foliomip3.mos
   ``````````````````
   Modeling a MIP problem 
   to perform portfolio optimization.
   -- Extending the problem with constraints on 
      the geographical and sectorial distributions --
   -- Working with a larger data set --
   
  (c) 2008 Fair Isaac Corporation
      author: S.Heipcke, Dec. 2008
*******************************************************!)

model "Portfolio optimization with MIP"
 uses "mmxprs"

 parameters
  MAXRISK = 1/3                     ! Max. investment into high-risk values
  MINREG = 0.2                      ! Min. investment per geogr. region
  MAXREG = 0.5                      ! Max. investment per geogr. region
  MAXSEC = 0.25                     ! Max. investment per ind. sector
  MAXVAL = 0.2                      ! Max. investment per share
  MINVAL = 0.1                      ! Min. investment per share
  MAXNUM = 7                        ! Max. number of different assets
                                    ! 6,7: difficult, 4: infeas, 5,8+:easy
  DATAFILE = "folio250.dat"         ! File with problem data
 end-parameters

 declarations
  SHARES: set of string              ! Set of shares
  RISK: set of string                ! Set of high-risk values among shares
  REGIONS: set of string             ! Geographical regions
  TYPES: set of string               ! Share types (ind. sectors)

  LOC: array(REGIONS) of set of string ! Sets of shares per geogr. region
  RET: array(SHARES) of real         ! Estimated return in investment
  SEC: array(TYPES) of set of string ! Sets of shares per industry sector
 end-declarations

 initializations from DATAFILE
  RISK RET LOC SEC
 end-initializations

 declarations
  frac: array(SHARES) of mpvar      ! Fraction of capital used per share
  buy: array(SHARES) of mpvar       ! 1 if asset is in portfolio, 0 otherwise
 end-declarations

! 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) <= MAXRISK

! Limits on geographical distribution
 forall(r in REGIONS) do
  sum(s in LOC(r)) frac(s) >= MINREG
  sum(s in LOC(r)) frac(s) <= MAXREG
 end-do 

! Diversification across industry sectors
 forall(t in TYPES) sum(s in SEC(t)) frac(s) <= MAXSEC

! 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

! Limit the total number of assets
 sum(s in SHARES) buy(s) <= MAXNUM

 forall(s in SHARES) do
  buy(s) is_binary                  ! Turn variables into binaries
  frac(s) <= MAXVAL*buy(s)                 ! Linking the variables
  frac(s) >= MINVAL*buy(s)                 ! Linking the variables
 end-do

! Display Optimizer log
 setparam("XPRS_verbose", true)

! Solve the problem
 maximize(Return)

! Solution printing
 writeln("Total return: ", getobjval)
 forall(s in SHARES | getsol(frac(s))>0)
  writeln(s, ": ", getsol(frac(s))*100, "% (", getsol(buy(s)), ")")

end-model 

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