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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 foliomiis.mos
   Modeling a MIP problem 
   to perform portfolio optimization.

   Same model as in foliomip3.mos.
   -- Infeasible model parameter values --
   -- Retrieving MIIS --
  (c) 2010 Fair Isaac Corporation
      author: S.Heipcke, Oct. 2010

model "Portfolio optimization with MIP"
 uses "mmxprs"

  MAXRISK = 1/4                     ! Max. investment into high-risk values
  MINREG = 0.1                      ! Min. investment per geogr. region
  MAXREG = 0.25                     ! Max. investment per geogr. region
  MAXSEC = 0.15                     ! Max. investment per ind. sector
  MAXVAL = 0.225                    ! Max. investment per share
  MINVAL = 0.1                      ! Min. investment per share
  MAXNUM = 5                        ! Max. number of different assets
  DATAFILE = "folio5.dat"           ! File with problem data

  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

 public 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
  Return: linctr                    ! Total return
  LimitRisk: linctr                 ! Max. percentage of high-risk values
  LimitMinReg,LimitMaxReg: array(REGIONS) of linctr  ! Min/max perc. per region
  LimitSec: array(TYPES) of linctr  ! Max. percentage per industry sector
  TotalOne: linctr                  ! Spend all the capital
  LimitNum: linctr                  ! Max. total number of assets
  LinkUB,LinkLB: array(SHARES) of linctr  ! Linking buy+frac variables

! Objective: total return
 Return:= sum(s in SHARES) RET(s)*frac(s) 

! Limit the percentage of high-risk values
 LimitRisk:= sum(s in RISK) frac(s) <= MAXRISK

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

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

! Spend all the capital
 TotalOne:= 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
 LimitNum:= sum(s in SHARES) buy(s) <= MAXNUM

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

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

! Uncomment this line to see that the problem is LP-feasible
! maximize(XPRS_LIN,Return)

! Solve the problem

  V: set of mpvar
  C: set of linctr

 probstat:= getprobstat
 case probstat of
  XPRS_OPT: writeln("Problem solved")
  XPRS_INF: do
              setparam("XPRS_verbose", false)  ! Disable Optimizer output
	      writeln("MIP infeasible")

              getiis({},{})                    ! Generate all IIS
              numiis:= getparam("XPRS_NUMIIS") ! Retrieve number of IIS
                                               ! (at most 1 for MIP)
              writeln("Total IIS:", numiis)
              forall(i in 1..numiis) do
               getiis(i, V, C)                 ! Retrieve the i'th IIS
               writeln("IIS ", i)
               write("  variables: "); writeln(getsize(V))
	       forall(v in V) write(getname(v), " "); writeln
               write("  constraints: "); writeln(getsize(C))
	       forall(c in C) write(getname(c), " "); writeln
  XPRS_OTH: writeln("Problem unbounded") 
  XPRS_UNF: writeln("Optimization unfinished")
  else writeln("Unknown problem status")


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