Advanced modelling and solving tasks for a portfolio optimization problem:
- Automated solver tuning (foliolptune.mos)
- Defining an integer solution callback (foliocb.mos, callback specification by name: foliocbm.mos; using an 'mpsol' object: foliocb_sol.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, MIP, NLP infeasible (folioiis.mos, foliomiis.mos, folionliis.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)
- main 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)
- main 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)