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Folio - Advanced modelling and solving tasks

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


   Mosel Example Problems

   file folioenumsol.mos
   Modeling a MIP problem 
   to perform portfolio optimization.
   Same model as in foliomip3.mos.
   -- Using the solution enumerator --
  (c) 2008 Fair Isaac Corporation
      author: S.Heipcke, Dec. 2008, rev. Jan. 2013

model "Portfolio optimization with MIP"
 uses "mmxprs"

  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
  DATAFILE = "folio10.dat"          ! File with problem data

 forward procedure print_sol

  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

 initializations from DATAFILE

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

! 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

! 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

! Remove doubles from the solution pool, comparing solutions on their
! discrete variables only

! Alternatively: switch off MIP heuristics to avoid generation of doubles
! setparam("XPRS_HEURSTRATEGY", 0)

! Disable presolve operations that attempt to improve the efficiency by
! cutting off MIP solutions from the feasible region
 presolveops:= getparam("XPRS_PRESOLVEOPS")
 if bittest(presolveops,32)=32 then presolveops-=32; end-if 
                                     ! Disable duplicate column removal
 if bittest(presolveops,8)=8 then presolveops-=8; end-if     
                                     ! Disable dual reduction operations
 setparam("XPRS_PRESOLVEOPS", presolveops)  
 presolveops:= getparam("XPRS_MIPPRESOLVE")
 if bittest(presolveops,16)=16 then presolveops-=16; end-if 
                                     ! Disable in-tree dual reductions
 setparam("XPRS_MIPPRESOLVE", presolveops)  
 setparam("XPRS_SYMMETRY", 0)        ! Disable symmetry detection

! Set the max. number of solutions to store (default: 10)
 setparam("XPRS_enummaxsol", 25)

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

! Solve the problem, enabling the solution enumerator
 maximize(XPRS_ENUM, Return)

! Print out all solutions saved by the enumerator
 forall(i in 1..getparam("XPRS_enumsols")) do
  selectsol(i)                      ! Select a solution from the pool
  writeln("Solution ", i)

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

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