Advances in Stochastic Mixed Integer Programming Lecture at the INFORMS Optimization Section Conference in Miami, Febru

Jan 17, 2013

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Some Historical RemarksClassification of SMIPSMIP Models: Risk, Recourse, Resilience Structural PropertiesDecomposition: Benders' and BeyondIllustrative Computational Results. Overview of this Lecture. Historical Remarks: IOS. Age of the INFORMS Optimization Section is0 < age 1010 < ag

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Advances in Stochastic Mixed Integer Programming Lecture at the INFORMS Optimization Section Conference in Miami, Febru

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Lecture 6 – Integer Programming Models

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Integer Programming

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Many uses of linear programming , mixed integer (linear) programming in this course

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Introduction to Mixed Integer Linear Programming

Introduction to Mixed Integer Linear Programming. L. 1. 2. 3. LP formulation of Economic Dispatch. P 1 MIN. x 1. x 3. x 2. P 1 MAX. P 2 MAX. P 3 MAX. P 2 MIN. Objective function is linear All constraints are linear All variables are real

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Integer Optimization

Integer Optimization. Summer 2013. Integer Optimization. In integer optimization, at least one variable is restricted to integer values. But the decision variables can be linear and / or nonlinear. We will consider only linear variables. Linear Optimization Classification.

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Conflict Analysis in Mixed Integer Programming

Conflict Analysis in Mixed Integer Programming. Main Idea. Problem is divided into smaller subproblems branching tree Some subproblems are infeasible Analyzing the infeasibilities can yield information about the problem Information can be used at other subproblems to prune the search tree.

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One Size Fits All? : Computational Tradeoffs in Mixed Integer Programming Software

One Size Fits All? : Computational Tradeoffs in Mixed Integer Programming Software. Bob Bixby, Mary Fenelon, Zonghao Gu, Ed Rothberg, and Roland Wunderling ILOG, Inc. A MIP Code is a Bag of Tricks. Presolve Cutting planes Gomory cuts Knapsack cuts Etc. Node presolve Heuristics

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Integer Optimization

Integer Optimization. Basic Concepts. Integer Linear Program(ILP): A linear program except that some or all of the decision variables must have integer values. LP Relaxation: A LP model that is derived from an ILP by dropping the integer requirements for the variables.

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Integer Programming

Integer Programming. Group Members Jayamalee M.W.A.N 061024 M Somarathna K.U.S. 061050 M Sooriyaarachchi S.L. 061052 V Thilakarathna M.D.G.L 061056 L. Introduction. Integer programming A sub category of Linear Programming

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Approximation of Non-linear Functions in Mixed Integer Programming

Approximation of Non-linear Functions in Mixed Integer Programming. TU Darmstadt. Alexander Martin. Workshop on Integer Programming and Continuous Optimization Chemnitz, November 7-9, 2004. Joint work with Markus Möller and Susanne Moritz.

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Stochastic Optimization in Electricity Systems

Stochastic Optimization in Electricity Systems. Andy Philpott The University of Auckland www.esc.auckland.ac.nz/epoc. Electricity optimization. Optimal power flow [Wood and Wollenberg, 1984,1996, Bonnans, 1997,1998] Economic dispatch [Wood and Wollenberg, 1984,1996] Unit commitment

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Integer Programming

Integer Programming. Li Xiaolei. Introduction to Integer Programming. An IP in which all variables are required to be integers is called a pure integer programming problem . For example, (1)

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INTEGER PROGRAMMING

INTEGER PROGRAMMING. 1.224J/ESD.204J TRANSPORTATION OPERATIONS, PLANNING AND CONTROL: CARRIER SYSTEMS Professor Cynthia Barnhart Professor Nigel H.M. Wilson Fall 2003. IP OVERVIEW. Sources: -Introduction to linear optimization (Bertsimas, Tsitsiklis)- Chap 1 - Slides 1.224 Fall 2000.

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5.2 Mixed Integer Linear Programming

5.2 Mixed Integer Linear Programming. 5.2.2 Implicit Enumeration. Assignment Problem. Assignment Problem. [Theorem ] Any basic feasible solution of the assignment problem has every xij equal to either zero or one. Implication: There are at most n variables that have the value 1.

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Mixed Integer Programming Models for Detailed Placement

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Stochastic programming in logistics

Stochastic programming in logistics. Stein W. Wallace The Chinese University of Hong Kong Molde University College, Norway. The goals of today. Show you some logistics problems Show why they are hard or simple Think a lot about options Discuss hard and soft constraints Discuss stages.

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Part 5 Mixed Integer Programming

Part 5 Mixed Integer Programming. 5.1 Introduction. Classification. Example 1: A Production Planning Problem. Example 1: MILP Formulation. Example 2: Minimum Number of Exchangers in HEN – MILP obtained by modifying the transportation model.

281 views • 26 slides

Deep Learning and Mixed Integer Optimization

Deep Learning and Mixed Integer Optimization. Matteo Fischetti, University of Padova. Machine Learning. Example (MIPpers only!): Continuous 0-1 Knapack Problem with a fixed n. of items. Implementing the ? in the box. Implementing the ? in the box. Deep Neural Networks (DNNs).

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Recent Advances in Query Optimization

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