Maximal: MPL Software Demo - INFORMS Phoenix Oct 2012

INFORMS Phoenix – October 2012

Deploying Optimization Models
on Servers and Mobile Platforms

Presented by
Bjarni Kristjansson
Maximal Software, Inc.

Copyright © 2012 Maximal Software, Inc. All rights reserved
1

Presentation Overview

• Global IT Trends affecting O.R.
• MPL Free Development/Academic Program
• Optimization Component Libraries

• New version of MPL OptiMax 4.2n
• MPL OptiMax code examples (Python / CSharp)
• Exception handling in MPL OptiMax

• Server-based and Cloud-based Optimization
• Web API’s and Web Services for Optimization

2

Well-Known Killer Applications

What are the two main differences between the applications on the left
column versus the right?

• VisiCalc • Firefox
• Lotus 1-2-3 • Napster
• Word Perfect • Google
• Harvard Graphics • YouTube
• DBase • Skype
• Turbo-Pascal • Facebook
• PageMaker • Twitter

We are Now in the Middle of the Web 2.0 Revolution!

3

Global Trends in IT

Globality
• 2.3 Billion Internet users in 2011 (+13% Y/Y)
• 670M 3G Subscribers (+37% Y/Y, 14% penetration)
• Facebook 1 Billion Users (+51% Y/Y)
• Tenecent [China] 637M Visitors (+31% Y/Y)

Mobile
• Mobile Internet (Apple: 120M users, 330K Apps)
• Android 59%, iOS 23%, Symbian 7%, RIM 6% (IDC Report)
• Mixi Mobile [Japan]: 84% page views vs. 16% Internet

4

Current Major Trends in Online IT

1. Mobile Devices are taking over personal computers
2. Social Media is replacing traditional news distribution
3. Social Networking is changing how people work together
4. Online Collaboration tools are replacing standalone software
5. Cloud computing is replacing traditional file-based documents
6. Online Chatting are replacing emails as effective communication tool
7. Text Messaging is replacing phone calls
8. Online Video services are replacing television viewing
9. Online Publishing: Books and published materials are going online
10. Virtual Education: Education and seminars are going virtual
11. Web sites are now expected to be personalized and engaging
12. Software Development: Servers, Mobile, Remote, Web API, Mashups

O.R. needs to adapt to this new changed world and
should effectively leverage these new IT technologies
5

Free Development Copies of MPL

Maximal is now offering several new special free and
subscription programs for MPL:
• Free Development Copies of MPL
• Free MPL with Solver Purchase
• Free MPL for Conference Participants
• 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

6

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 (CBC/CLP) solver
• Can be used with all other free academic solvers
supported by MPL (CPLEX, GUROBI, XPRESS)
• Mainly intended for classroom and research purposes:
Educational Research
Classroom Teaching
Student Projects
• Includes new Stochastic Programming and Nonlinear extensions

• To apply, please go to and fill out the application form:

www.maximalsoftware.com/Academic
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

Programming vs. Optimization Modeling

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

9

Optimization Component Library

Program
Program

OptiMax
OptiMax DB
DB

Application
Optimization

ModLang
ModLang

Solver
Solver

10

OptiMax Component Library
Seamless Integration of Technologies

• Modeling Language
• Solver/Optimizers
• Database/Data Sources
• Programming Languages
• Visual Basic/VBA
• C/C++
• CSharp/VB.Net
• Python
• Java
• Delphi
• Web scripting

11

Object Model Hierarchy

OptiMax

Solvers (Solver)

SolverOptions (SolverOption)

MplOptions (MplOption)

StatusWindow

Models (Model)

Matrix

Variables (Variable)

Constraints (Constraint)

Solution

Variables (Variable)

Constraints (Constraint)

Symbols (Symbol)

12

Object Model - Symbols

OptiMax

Models (Model)

Symbols (Symbol)

IndexSets (IndexSet)

DataConstants (DataConstant)

DataVectors (DataVector)

Subscripts (Subscript)

Macros (Macro)

VariableVectors (VariableVector)


ConstraintVectors (ConstraintVector)


PlainVariables (Variable)

PlainConstraints (Constraint)

13

Application Building Features

• General Model Handling
• Model Parsing
• Model Querying
• Modifying Models / Solving Models
• Write Standard Output Files
• Database Import/Export
• Multiple Models in Memory
• Multiple Model Runs / Iterations over models
• Use Solution Values as Input
• Callbacks/User Exits
• Access to MPL Internal Indexing Data Structures

14

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/Phyton
• 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
15

New release of MPL OptiMax

New MPL Release 4.2n:
• 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 Python (PYD files)
MPL Connection for Matlab (MEX files)
• Seamless integration with native data structures - Add()
• Full exception handling with detailed error messages
• ToString() / str() implementation for all classes
• Lot of new MPL and OptiMax code samples
• C-API interface with Mpllib42.dll with over 600 functions
• MPL OptiMax server version with full multi-threaded support

16

MPL OptiMax 4.2n for .Net

• Supports both CSharp and Visual Basic (VS 2008/2010 -
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

17

MPL OptiMax 4.2n for Python

• Supports Python 2.6, 2.7, 3.1, and 3.2 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

18

MPL OptiMax 4.2n for Matlab

• Supports MATLAB in both 32-bit and 64-bit mode
• Distributed as compiled MEX files
• Utilizes the new C-API Mpllib42.dll library
• 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

19

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))

20

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());

21

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))

22

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
23

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)

24

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

25

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

26

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;
^

27

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.

28

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

29

Optimization on Mobile Devices

Optimization on the mobile device (thick clients)
• Model (device)
• Solvers (device)
• Data (device / server)

Example: TSP Concord on iPad

Optimization on the server (thin clients)
• Model (server)
• Solvers (server)
• Data (device / server)

30

MPL OptiMax on Optimization Servers

Client-Side Technologies:
• AJAX
• XMLHttpRequest
• XHTML and CSS
• Document Object Model (DOM)
• JavaScript
• XML and XSLT

• Mobile / Tablets
• iPhone / iPad (Objective-C)
• Android (Java)
• HTML5

31

Cloud-based Optimization

Example:
Large energy company needs to run 2000 model instances,
each taking 1-2 hours to solve, within a day
• Traditional standalone computer approach
• Maintain large number machines
• Install optimization and modelling software
• Run model instances over the network
• Very expensive fixed costs upfront

• Cloud-based computing approach
• Setup account on Cloud, such as Amazon EC2
• Write script to run the models on cloud
• Cost is typically calculated per core, per hour.

32

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

33

Google Visualization API – Portfolio Opt.

34

Google Maps API - Shortest Path Optimization

35

Maximal: MPL Software Demo - INFORMS Phoenix Oct 2012

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