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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 foliolptune.mos
   Modeling a small LP problem 
   to perform portfolio optimization.
   -- Added Tuner flags --
  (c) 2008 Fair Isaac Corporation
      author: S.Heipcke, Aug. 2003

model "Portfolio optimization with LP"
 uses "mmxprs"                       ! Use Xpress Optimizer
 uses "mmsystem"

 public declarations
  SHARES = 1..10                     ! Set of shares
  RISK = {2,3,4,9,10}                ! Set of high-risk values among shares
  NA = {1,2,3,4}                     ! Set of shares issued in N.-America
  RET: array(SHARES) of real         ! Estimated return in investment

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

 RET:: [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

(! Problem output from Mosel (to check problem definition, not for tuning):
  exportprob(EP_MAX,'foliomos.lp', Return);
  exportprob(EP_MPS,'foliomos.mat', Return);

! Problem output from the solver (for tuning):
  writeprob('folioopt.mat', 'x');

! Display progress log

! Solve the problem
 setparam("XPRS_TUNERMAXTIME", 60)
 setparam("XPRS_TUNEROUTPUTPATH", string(expandpath("TuneOut")))
 maximize(XPRS_TUNE, Return)

  status:array({XPRS_OPT,XPRS_UNF,XPRS_INF,XPRS_UNB,XPRS_OTH}) of string

          "Optimum found","Unfinished","Infeasible","Unbounded","Failed"]
 writeln("Problem status: ", status(getprobstat))
! Solution printing
 writeln("Total return: ", getobjval)
 forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")  


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