New Release 5.0 of MPL and OptiMax Library - OR Vienna 2015

1
Copyright © 2015 Maximal Software, Inc. All rights reserved
OR Vienna – Sept 2015
Introducing New Release 5.0
of the MPL Modeling System and
the OptiMax Component Library
Presented by
Bjarni Kristjansson
Maximal Software, Inc.

2
New MPL Release 5.0
MPL Modeling System 5.0
• New Directory Structure for MPL
• New Documentation for MPL
• Updates for New Solver Releases
• Reverse Hessian Algorithm for Solving Nonlinear Models
• Check Feasibility for the Solution returned by the Solver
• Support for VS 2012, 2013, Python 3.3, 3.4, Java 8
• Enhancements and additions for MPL OptiMax
• New Callbacks for MPL OptiMax and MPL Callable Library
• New Exception Handlers for MPL OptiMax
• Thread-safe for multi-threaded applications
• New Example Files for MPL OptiMax and Callable Library

3
New Directory Structure for MPL
Mplwin50
• bin
• Win32
• Win64
• docs
• examples
• Models
• Mpllib
• OptiMax
• include
• setup
• Matlab
• Python
MplwinModels
• GlobalModels
• Stochastic
• Tutorial

4
New Documentation for MPL
MPL Documentation provided in 10 separate user-guides published
in 6 different output formats (PDF, Help, Web, ePub, iPad, Kindle)
- Implemented in XML and Python - Pythonified, NOT Unified! 
• MPL for Windows User Guide
• MPL Language Reference
• MPL Option Parameters
• MPL Online Tutorial
• MPL 5.0 Release Notes
• MPL OptiMax Library for Python
• MPL OptiMax Library for CSharp
• MPL OptiMax Library for Visual Basic
• MPL OptiMax Library for COM (VBA)
• MPL Callable Library

5
New Documentation for MPL (Solvers)
Plus 15 different solver guides for each supported solver:
• CPLEX Solver for MPL
• GUROBI Solver for MPL
• XPRESS Solver for MPL
• LINDO Solver for MPL
• SULUM Solver for MPL
• MOSEK Solver for MPL
• XA Solver for MPL
• MOPS Solver for MPL
• FortMP Solver for MPL
• CoinMP Solver for MPL
• GLPK Solver for MPL
• LPSolve Solver for MPL
• CONOPT Solver for MPL
• KNITRO Solver for MPL
• LGO Solver for MPL

6
MPL for Windows 5.0 – New Solver Support
• Cplex 12.6 (LP, MIP, BAR, MIQP)
• Gurobi 6.0 (LP, MIP, BAR, MIQP)
• Xpress 18.0 (LP, MIP, BAR, MIQP)
• Lindo 9.0 (LP, MIP , BAR, MIQP, NLP, MINLP, SP)
• Sulum 4.3 (LP, MIP)
• Mosek 7.1 (LP, MIP, BAR)
• XA 16.0 (LP, MIP)
• CoinMP 1.7 (LP, MIP)
• GLPK 4.52 (LP, MIP, BAR)
• LPSolve 5.5 (LP, MIP)
• Conopt 3.0 (LP, NLP)
• Knitro 9.0 (LP, MIP, NLP, MINLP)
• Ipopt 3.11 (LP, NLP)
• LGO (NLP Global)

7
MPL OptiMax Applications
Phase I - Development
• Model Development Environment
• Formulating the Model
• Connecting to External Data
• Solver Testing and Tuning
Phase II - Deployment
• End-User Applications
• Run-times / Component Libraries
• Embedding Optimization into Application
• Packaging/Distribution

8
Application Programming
• Programming Languages (Procedural)
• Application/User-Interface
• Database Programming
• Most programmers tend to know rather little about
optimization and formulating models
Optimization Modeling
• Modeling Languages (Declarative)
• Optimization Packages
• Most model builders tend to know rather little about
application/GUI and database programming
Programming vs. Optimization Modeling

9
DBDB
ProgramProgram
SolverSolver
ModLangModLang
OptiMaxOptiMax
Application
Optimization
Optimization Component Library

10
New release 5.0 of MPL OptiMax
MPL OptiMax Library
• Multiple new objects, methods, and properties for OptiMax
• Full support for both 32-bit and 64-bit platforms
MPL OptiMax for .Net (CSharp / VB.Net)
MPL OptiMax for Java (JAR files)
MPL OptiMax for Python (PYD files)
MPL Connection for Matlab (MEX files)
• Seamless integration with native data structures - Add()
• Exception handling with detailed error messages
• Callback support

11
MPL OptiMax 5.0 for .Net
• Supports both CSharp and Visual Basic
(VS 2008/2010/2012/2013 - 32/64 bit)
• Multiple new objects, methods and properties
• Seamless integration with native .Net data structures
• Array
• ArrayList
• Generic List
• ICollection
• IEnumerable
• Iterators for all collection classes to support foreach loops
• ToString() implementation for all classes

12
MPL OptiMax 5.0 for Python
• Supports Python 2.6, 2.7, 3.1, 3.2, 3.3, and 3.4
in both 32-bit and 64-bit mode
• Distributed as compiled PYD files for increased speed
• Multiple new objects, methods and properties
• Seamless integration with native python data structures
• Lists, List of Lists
• Tuples, Tuples of Tuples
• Iterators for all collection classes to support for loops
• Implementation of magic methods for all classes
__init__(), __len__(), __iter__(), __getitem__(), __str__()
• Dynamic object attributes for model identifiers

13
New Callbacks for MPL OptiMax 5.0
Callback support for reporting status and interaction between
the calling application and MPL/solvers
• Message Log Callback
• Request Data Value Callback
• Status Header Callback
• Status Counter Callback
• Status Filename Callback
• Status Memory Used Callback
• Status Algorithm Callback
• Solver Message Log Callback
• Solver LP Iteration Callback
• Solver Presolve Callback
• Solver MIP Node Callback
• Solver MIP Improve Callback
• Solver Terminate Callback

14
New Exception Handlers for MPL OptiMax 5.0
Exception handling with detailed error messages
• CollectionIndexTypeException
• CollectionNotChangedIndexException
• CollectionValidNameIndexException
• CollectionValidRangeIndexException
• MethodArgEnumIndexException
• MethodArgListTypeException
• MethodArgTypeException
• MethodRuntimeException
• ModelResultException
• ModelAddException
• PropertyTypeException (Python)
• PropertyValidRangeIndexException

15
MPL OptiMax 5.0 for Matlab
MPL Matlab MEX interface
• Supports MATLAB in both 32-bit and 64-bit mode
• Utilizes the MPL Callable Library (mpllib.dll)
• Based on the standard interface of MATLAB Optimization
Toolbox
• mpl_initlibrary()
• mpl_linprog()
• mpl_bintprog()
• mpl_mintprog()
• mpl_freelibrary()
• MPL can be kept in memory through the hmpl library handle
• Solver can be specified through the solverName argument
• Solver options can be set through the option list argument

16
MPL Callable Library 5.0
MPL C-API DLL Library:
• Full C-Api library interface with close to 500 functions
• Available in both 32-bit and 64-bit versions
• Utilized by MPL OptiMax Component Libraries
• MPL OptiMax for .Net
• MPL OptiMax for Python
• MPL OptiMax for Matlab
• Supports all functionality of MPL OptiMax from the C language
• Thread-safe for multi-threaded applictions
• Callback Handling

17
Simple MPL OptiMax Example in Python
from mplpy import *
def RunModel(modelName):
modelFile = mpl.HomeDir + modelName + ".mpl"
print("MPL file: " + modelFile)
mod.ReadModel(modelFile)
print("Variables = " + str(mod.Matrix.Variables.Count))
print("Constraints = " + str(mod.Matrix.Constraints.Count))
print("")
print("Solving " + modelName + " with " + mpl.Solver.Name + " solver:")
mod.Solve(mpl.coinmp)
print("")
print(mod.Solution.ResultString)
print("ObjectValue = " + str(mod.Solution.ObjectValue))
for var in mod.Solution.Variables:
print(var.Name + " = " + str(var.Activity))

18
Planning Example in CSharp
OptiMax mpl = new OptiMax();
mpl.UseExceptions = false;
Solver solver = mpl.Solvers.Add("CPLEX");
Model model = mpl.Models.Add("Planning");
string modelFile = mpl.HomeDirectory + modelName + ".mpl";
Console.WriteLine("Reading MPL file: " + modelFile);
ResultType result = model.ReadModel(modelFile);
if (result != ResultType.Success)
{
Console.WriteLine(model.Error.ToString());
}
Console.WriteLine("Solving " + modelName + " with " + solver.Name + " solver");
result = model.Solve(solver);
foreach (Variable var in model.Solution.Nonzeros)
{
Console.WriteLine(var.Name + " " + var.Activity.ToString());
}
Console.WriteLine(model.Solution.ResultString +
" ObjectValue = " + model.Solution.ObjectValue);
Console.WriteLine("Nonzeros = " + model.Solution.Nonzeros.Count.ToString());

19
Portfolio Example - Dynamic Attributes
from mplpy import mpl
from numpy import arange, float32
from matplotlib import pyplot as plot
modelFilename = mpl.HomeDir + "Portfolio.mpl"
result = mpl.model.SolveModel(modelFilename, mpl.cplex)
investVect = mpl.model.Invest
investVect.ZeroTol = 0.001
count = investVect.Nonzeros.Count
investAmount = arange(count, dtype=float32)
stockNames = range(count)
for i, var in enumerate(investVect.Nonzeros):
investAmount[i] = var.Activity
stockNames[i] = investVect.stock.ValueStr
print((stockNames[i] + ":").ljust(8) +
("%1.1f%%" % (investAmount[i] * 100.0)).rjust(6))

20
EFrontier Example - Changing RHS
iterCount = 100
deltaPercent = 0.02
CPLEX_OPTIMAL = 1
targetReturnData = model.TargetReturn
varianceMacro = model.Variance
meetTargetCon = model.MeetTarget
TargetArray = arange(iterCount, dtype=float32)
VarianceArray = arange(iterCount, dtype=float32)
for i in range(iterCount):
target = targetReturnData.Value * (1 + i * deltaPercent)
meetTargetCon.RHSValue = target
model.Solve(mpl.cplex)
print(str(i) + ") " + str(target) + ", " + str(varianceMacro.Value))
if model.Solution.ResultCode == CPLEX_OPTIMAL:
TargetArray[i] = target
VarianceArray[i] = varianceMacro.Value
else:
solCount = i
print(str(solCount + 1) + ") " +
model.Solution.ResultString + " (" +
str(model.Solution.ResultCode) + ")")
break

21
Add Statements - IndexSet / DataVectors
Adding IndexSet:
cutNames = ['w1', 'w2', 'w3', 'w4', 'w5', 'w6', 'w7', 'w8']
model.IndexSets.AddNameSet("cuts", cutNames)
Adding DataVectors:
cutDemand = [500, 400, 300, 450, 350, 200, 800, 200]
model.DataVectors.AddDense("CutDemand[cuts]", cutDemand)
cutsInPattern = ['p1', 'w1', 1,
'p1', 'w8', 1,
'p2', 'w2', 1,
'p2', 'w7', 1,
. . .
'p28', 'w8', 5,
'p29', 'w8', 7]
model.DataVectors.Add("CutsInPattern[patterns, cuts]", cutsInPattern)

22
CutStock Example – Add() Statements
def LoadModel(self, model, cutNames, patternNames, priceSheet,
sheetsAvail, cutDemand, cutsInPattern):
try:
model.IndexSets.AddNameSet("cuts", cutNames)
model.IndexSets.AddNameSet("patterns", patternNames)
model.DataConstants.Add("PriceSheet", priceSheet)
model.DataConstants.Add("SheetsAvail", sheetsAvail)
model.DataVectors.AddDense("CutDemand[cuts]", cutDemand)
model.DataVectors.Add("CutsInPattern[patterns, cuts]",
cutsInPattern)
model.ReadFilePart("Cutstock.mpl", 19);
except Exception as ex:
print(str(ex))
result = model.LastResult
return result

23
CutStock Example – Add() Statements
def LoadModel(self, model, cutNames, patternNames, priceSheet,
sheetsAvail, cutDemand, cutsInPattern):
try:
. . . # previous model index and data Add() statements
model.PlainVariables.Add("SheetsCut", "-> T1")
model.PlainVariables.Add("TotalCost", "-> TC")
model.VariableVectors.Add("PatternCount[patterns]", "-> """)
model.VariableVectors.Add("ExcessCuts[cuts]", "-> X")
model.Objectives.Add("TotalCost", ObjectSense.Minimize)
model.PlainConstraints.Add("TotCost", "TotalCost =
PriceSheet*SheetsCut")
model.PlainConstraints.Add("RawAvail", "SheetsCut < SheetsAvail")
model.PlainConstraints.Add("Sheets",
"SheetsCut = SUM(patterns: PatternCount[patterns])")
model.ConstraintVectors.Add("CutReq[cuts]",
"SUM(patterns: CutsInPattern[patterns, cuts] *
PatternCount[patterns]) =
CutDemand[cuts] + ExcessCuts[cuts]")
except Exception as ex:
print(str(ex))
result = model.LastResult
return result

24
Exceptions with Detailed Syntax Error Messages
Syntax Error:
model.PlainConstraints.Add("TotCost", "TotalCost = PriceSheet */ SheetsCut")
Exception Output:
The PlainConstraints.Add('TotCost', expr='TotalCost = PriceSheet */ SheetsCut') method
returned 'SyntaxError' (2) with the following error message:
**** A minor mistake was found in line 1:
6. I expected to see either a number or a variable, but found instead '/'.
File: C:MplWin4OptiMaxPythonCutStock
Line: (line=1, col=43)
SUBJECT TO TotCost: TotalCost = PriceSheet */ SheetsCut;
^

25
Exceptions with Wrong Data Types
Syntax Error:
cutNames = ['w1', 'w2', 'w3', 'w4', 'w5', 'w6', 'w7', 'w8']
model.DataConstants.Add("SheetsAvail", cutNames)
Exception Output:
The argument 'dataValue' for the 'DataConstants.Add('SheetsAvail', dataValue, dataAttr)' method
cannot be assigned the value '['w1', 'w2', 'w3', 'w4', 'w5', 'w6', 'w7', 'w8']' of type 'list'.
Quick fix: Please enter either a numeric or string value.

26
Copyright © 2015 Maximal Software, Inc. All rights reserved 26
Server-Based Optimization
Optimization is well-suited for running on Servers
• Well-defined input (model) and output (solution)
• Optimization algorithms require serious computing power
• Robust standards-based programming libraries
• Easy deployment in multiple locations
• Easier to update / maintain the model
• Hardware can be easily upgraded
• CPU / Memory
• Less overall cost
The old IT model of a single person, running a single
application on a single machine is going away fast

27
MPL OptiMax on Optimization Servers
Full support for multi-threaded applications
• Optimization Servers
• Web Services (REST / SOAP)
• Web API’s
• Mobile / Tablet Applications
Support for server-side application frameworks
• ASP.Net
• Python

28
Google Web API Toolkits
• Google Drive (Google Apps)
• Google Spreadsheets API
• Full control of visibility and who can update
• Built-in Javascript
• Support SQL types of queries
• Google Query API
• Google Visualization API
• Bar charts, Pie charts, Line charts and many more
• Very customizable
• Data Tables and Data Views
• Google Maps API
• Add markers, Polylines and Info Windows

29
Google Visualization API – Portfolio Opt.

30
Google Maps API - Shortest Path Optimization

31
Applications with MPL OptiMax
• Model written in MPL
• Data transferred to/from MPL through
• Excel Ranges
• Database Connection
• Text files
• End-User Application Building
• User-Interface done in VB/CSharp/C/C++/Java/Python
• Process Input from User
• Control Solver Runs
• Process Output from Model
• Display graphs
• Model solved with any supported solver
No limits on size, speed, or robustness

32
Free Development Copies of MPL
Maximal Software offers several new special free
and subscription programs for MPL:
• Free Development Copies of MPL
• Free MPL Software for Academics
• Pay Maintenance Only
• Subscription-based Pricing
• Free Open-source Solvers
• Affordable Runtime / Floating Licenses
• Free Model Conversion to MPL
• To apply, please go to and fill out the application form:
www.maximalsoftware.com/FreeDev

33
Academic Program from Maximal
Academic Program Highlights
• Free full-size MPL version for Academic Users
• Fully functional time-limited versions of MPL
• Distributed with the full-size open source CoinMP and IPOPT solvers
• Can be used with several other free academic solvers
supported by MPL (CPLEX, GUROBI, XPRESS, SULUM)
• Mainly intended for classroom and research purposes:
Educational Research
Classroom Teaching
Student Projects
• Includes the Stochastic Programming and Nonlinear extensions
• To apply, please go to and fill out the application form:
www.maximalsoftware.com/Academic

New Release 5.0 of MPL and OptiMax Library - OR Vienna 2015

Recommended

Recommended

More Related Content

What's hot

What's hot (14)

Similar to New Release 5.0 of MPL and OptiMax Library - OR Vienna 2015

Similar to New Release 5.0 of MPL and OptiMax Library - OR Vienna 2015 (20)

More from Bjarni Kristjánsson

More from Bjarni Kristjánsson (7)

Recently uploaded

Recently uploaded (20)

New Release 5.0 of MPL and OptiMax Library - OR Vienna 2015