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Modeling and problem solving with Xpress-Mosel FICO Xpress Training S. Heipcke Xpress Team, FICO http://www.fico.com/xpress The material in this presentation is the property of Fair Issac Corporation, is provided for the recipient only, and shall not be used, reproduced, or disclosed without Fair Isaac Corporation’s express consent. c○2010 Fair Issac Corporation.

  • Page 2 and 3: Introduction, Xpress overview c○2
  • Page 4 and 5: Aims » At the end of the course yo
  • Page 6 and 7: Overview of Xpress c○2010 Fair Is
  • Page 8 and 9: Optimization algorithms Optimizer S
  • Page 10 and 11: Mosel » A modeling and solving env
  • Page 12 and 13: Mosel: Components and interfaces »
  • Page 14 and 15: Xpress-IVE » Visual Studio style v
  • Page 16 and 17: Why use modeling software? c○2010
  • Page 18 and 19: Why use modeling software? » Devel
  • Page 20 and 21: Xpress-IVE demonstration c○2010 F
  • Page 22 and 23: Xpress-IVE demonstration » Editor:
  • Page 24 and 25: Xpress-IVE demonstration » Output
  • Page 26 and 27: Xpress-IVE demonstration » Debugge
  • Page 28 and 29: The material in this presentation i
  • Page 30 and 31: Overview » Modeling basics » Acce
  • Page 32 and 33: Topics » Definition of decision va
  • Page 34 and 35: Example: Chess problem » Each of t
  • Page 36 and 37: Chess problem: Graphical solution x
  • Page 38 and 39: Starting and ending a Mosel model m
  • Page 40 and 41: Decision variables » mpvar means m
  • Page 42 and 43: Bounds on decision variables » Var
  • Page 44 and 45: Constraints » The ‘value’ of a
  • Page 46 and 47: Optimization & matrix generation »
  • Page 48 and 49: Project work [C-1]: Chess problem
  • Page 50 and 51: Solution: Completed model Chess 1 m
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    LP solution analysis » What is the

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    Solution analysis » Limit the amou

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    Data structures » Set: unordered c

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    Data declaration declarations NWEEK

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    Summations » Sum up an array of va

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    Loops » Use do/end-do to group sev

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    Conditions » May include condition

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    Model building style » You should

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    Model building style » Suggestion:

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    Model building style » Using named

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    Model building style » Comments ar

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    Accessing data sources » The initi

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    Separation of problem logic and dat

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    Data file chess.dat » Every data i

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    Sparse data format » Format of dat

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    Writing data out to text files decl

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    Project work [C-2]: Arrays and inde

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    Dynamic arrays » Dynamic array: in

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    Dynamic arrays of decision variable

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    Dynamic arrays » Use dynamic array

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    Data input from file: Chess 4 compl

  • Page 94 and 95:

    Run-time parameters » The value in

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    Run-time parameters » Parameters a

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    Project work [C-3]: Run-time parame

  • Page 100 and 101:

    Using other data sources » The ini

  • Page 102 and 103:

    Data transfer using ODBC » Next, i

  • Page 104 and 105:

    Reading data via ODBC » Excel spre

  • Page 106 and 107:

    Special notes for data export to Ex

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    Data exchange with Oracle » Softwa

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    Project work [C-4]: ODBC » Check t

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    Reference material » Refer to the

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    Topics » MIP variable types » Mod

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    MIP variable types » Integer varia

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    MIP variable types » Semi-continuo

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    MIP variable types » Special Order

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    SOS definition » Alternative: set

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    Disjunctions » Either or 5 ≤ x

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    Disjunctions » Either 5 ≤ ∑ i

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    Absolute values » Introduce binary

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    Project work [C-5]: Logical constra

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    Programming language features » Se

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    Selections » if if A >= 20 then x

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    Example: Prime numbers » Implement

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    Example: Prime numbers n:=2 repeat

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    Functions and procedures » Similar

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    Example: Quick Sort algorithm model

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    Example: Quick Sort algorithm ! Sor

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    Data structures » array » set »

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    List » Collection of objects of th

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    User types » Treated in the same w

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    Summary: Language features » Data

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    Solving: Variable fixing heuristic

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    Solving: (model) Variable fixing he

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    Solving: Variable fixing heuristic

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    Mosel: A modular environment » Ope

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    Mosel: A modular environment » Mod

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    Module mmxprs: Using callback funct

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    Module mmxslp: Solving an NLP by SL

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    Module kalis: Constraint Programmin

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    Module kalis: Constraint Programmin

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    Module mmive: Drawing user graphs f

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    c○2010 Fair Issac Corporation.

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    Reference material » The modules o

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    Embedding Mosel models c○2010 Fai

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    What is the Mosel API? » The Mosel

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    Mosel libraries » Model Compiler L

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    Generating a deployment template »

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    Mosel library functions » General:

  • Page 188 and 189:

    Project work [C-6]: Model deploymen

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    Solution // Run model model.execPar

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    Extending the example // Get the fi

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    Extending the example } } xprm = ne

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    Summary » May choose to work with

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    The material in this presentation i

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    Summary c○2010 Fair Issac Corpora

  • Page 202 and 203:

    Summary » Have seen: » FICO Xpres

  • Page 204 and 205:

    Summary » Have seen: » FICO Xpres

  • Page 206 and 207:

    Summary » Have seen: » Modeling w

  • Page 208 and 209:

    Summary » Have seen: » Modeling w

  • Page 210 and 211:

    Further information » Xpress websi

  • Page 212:

    www.fico.com/xpress The material in

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