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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





folioqpvar.xml


 0.1
 19
 -2
 4
 1
 1
 1
 0.5
 10
 5
 -2
 28
 1
 2
 1
 1
 -2
 -1
 4
 1
 22
 1
 2
 3
 4
 1
 2
 4
 -1.5
 -2
 -1
 1
 1
 1
 1
 1
 -1.5
 3.5
 2
 0.5
 1
 1.5
 1
 1
 2
 -2
 2
 5
 0.5
 1
 2.5
 0.5
 -1
 0.5
 0.5
 1
 0.5
 0.5
 10
 -2
 3
 1
 1
 1
 0.5
 25
 8
 5
 -1
 4
 1
 1.5
 2.5
 0.5
 8
 16


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