Folio - Modelling examples from 'Getting started'
Description
- Chapter 3 Inputting and Solving a Linear Programming problem
- foliolp.mos: modeling and solving a small LP problem
- foliolperr.mos: LP model with syntax errors
- foliolps.mos: LP model using string indices
- Chapter 4 Working with data
- foliodata.mos (data file: folio.dat): data input from file, result
output to a file, model parameters
- folioodbc.mos (data files: folio.xls, folio.mdb, folio.sqlite): data input
from a spreadsheet or database, result output to a spreadsheet or database,
model parameters
- folioexcel.mos (data file: folio.xls): same as folioodbc.mos but
with Excel-specific data input and output (Windows only)
- foliosheet.mos (data file: folio.xls): same as folioodbc.mos but
with data input and output through generic spreadsheet access
- foliocsv.mos (data file: folio.csv): same as folioodbc.mos but
with data input and output through generic spreadsheet access in CSV format
- Chapter 5 Drawing user graphs
- folioloop.mos (data files: folio.dat, foliodev.dat): re-solving with
varied parameter settings
- folioloop_graph.mos (data files: folio.dat, foliodev.dat): re-solving with
varied parameter settings, graphical solution display
- foliolps_graph.mos: same as foliolps, adding graphical solution display
- Chapter 6 Mixed Integer Programming
- foliomip1.mos (data file: folio.dat): modeling and solving a small
MIP problem (binary variables)
- foliomip2.mos (data file: folio.dat): modeling and solving a small
MIP problem (semi-continuous variables)
- Chapter 7 Quadratic Programming
- folioqp.mos (data file: folioqp.dat): modeling and solving a QP and
a MIQP problem
- folioqp_graph.mos (data files: folioqp.dat, folioqpgraph.dat):
re-solving a QP problem with varied parameter settings, graphical solution
display
- folioqc.mos (data file: folioqp.dat): modeling and solving a QCQP and
- foliomiqc.mos (data file: folioqp.dat): modeling and solving a MIQCQP
- Chapter 8 Heuristics
- folioheur.mos (data file: folio.dat): heuristic solution of a MIP problem
Source Files
By clicking on a file name, a preview is opened at the bottom of this page. Data Files
foliolps.mos
(!******************************************************
Mosel Example Problems
======================
file foliolps.mos
`````````````````
Modeling a small LP problem
to perform portfolio optimization.
-- Using string indices --
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Mar. 2006
*******************************************************!)
model "Portfolio optimization with LP"
uses "mmxprs"
declarations
! Set of shares
SHARES = {"treasury", "hardware", "theater", "telecom", "brewery",
"highways", "cars", "bank", "software", "electronics"}
! Set of high-risk values among shares
RISK = {"hardware", "theater", "telecom", "software", "electronics"}
! Set of shares issued in N.-America
NA = {"treasury", "hardware", "theater", "telecom"}
RET: array(SHARES) of real ! Estimated return in investment
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
RET::(["treasury", "hardware", "theater", "telecom", "brewery", "highways",
"cars", "bank", "software", "electronics"])[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
! Solve the problem
maximize(Return)
! Solution printing
writeln("Total return: ", getobjval)
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
end-model
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