'Dual problem' presentation slideshows

Vehicle Circulation and the Hungarian Method

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10701 Recitation 5 Duality and SVM

10701 Recitation 5 Duality and SVM. Ahmed Hefny. Outline. Langrangian and Duality The Lagrangian Duality Examples Support Vector Machines Primal Formulation Dual Formulation Soft Margin and Hinge Loss. Lagrangian. Consider the problem s.t.

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Support Vector Machine (SVM) Classification

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DMOR

DMOR. DEA. Variable Returns to Scale. O 7. O 2. O 6. OR. O 3. O 4. O 2. Constant Returns to Scale. O 1. DEA example. For each bank branch we have one output measure and one input measure. Efficiency. Inputs are changes into outputs. Relative efficiency.

591 views • 45 slides

EE 685 presentation

Utility-Optimal Random-Access Control By Jang-Won Lee, Mung Chia ng and A. Robert Calderbank. EE 685 presentation. Objective of the paper. Aims to design medium access control (MAC) protocols for wireless networks through the network utility maximization (NUM) framework.

412 views • 30 slides

Andrea Frome, Yoram Singer, Fei Sha, Jitendra Malik

Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification. Andrea Frome, Yoram Singer, Fei Sha, Jitendra Malik. Goal. Nearest neighbor classification. D ( , ). Learning a Distance Metric from Relative Comparisons.

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Capacitated vertex covering

Capacitated vertex covering. Guha, Hassin, Khuller, Or. Problem outline A simple 4-approximation via LP-rounding 2-approximation by a primal-dual algorithm 3-Approximation with edge demands Capacitated covering on trees. ??? ???. Vertex covering ? ???? ?.

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Domestic & Family Violence and Complex Trauma: Where do we start?

Domestic & Family Violence and Complex Trauma: Where do we start?. Met-West Violence Prevention Network Conference , 4 November 2010 Sarah Stewart Education Centre Against Violence.

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GAMS: General Algebraic Modeling System Linear and Nonlinear Programming

GAMS: General Algebraic Modeling System Linear and Nonlinear Programming. The lectures are based on: -- McCarl, Bruce A., and Thomas H. Spreen . Applied Mathematical Programming Using Algebraic Systems. Available at: http://agecon.tamu.edu/faculty/mccarl/regbook.htm

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Integration II

300 views • 23 slides

The Dual Problem: Minimization with problem constraints of the form ?

The Dual Problem: Minimization with problem constraints of the form ?.

394 views • 23 slides

Graduate Program in Business Information Systems

Graduate Program in Business Information Systems. Linear Programming: Sensitivity Analysis and Duality Asl? Sencer. Shadow Prices and Opportunity Costs. LP solution answers the tactical question, i.e., how much to produce Suppose the focus is on resources rather than the products, i.e.,

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Quadratic Programming and Duality

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DUALITY

DUALITY. IENG511 Optimization Theory Assis.Pro.Dr.Sahand Daneshvar Ramin Soufi Spring 2013-14. DUALITY. Associated with each linear programming problem there is another linear programming problem called the dual.

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WOOD 492 MODELLING FOR DECISION SUPPORT

WOOD 492 MODELLING FOR DECISION SUPPORT. Lecture 13 Duality Theory. The Dual Problem. Another way of looking at a linear program Has the same optimal value for the objective function as the primal model

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Chap 9. General LP problems: Duality and Infeasibility

Chap 9. General LP problems: Duality and Infeasibility. Extend the duality theory to more general form of LP Consider the following form of LP. subject to. (1). (2). Want to define dual problem for this LP so that dual objective value gives upper bound on the primal optimal value.

781 views • 11 slides

Linear Programming Supplements (Optional)

Linear Programming Supplements (Optional). Standard Form LP (a.k.a. First Primal Form). Strictly ?. All x j 's are non-negative. Transforming Problems into Standard Form. Min c T x ? Max - c T x Max ( c T x + constant) ? Max c T x

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ECE 665 Spring 2004 Computer Algorithms with Applications to VLSI CAD

ECE 665 Spring 2004 Computer Algorithms with Applications to VLSI CAD. Linear Programming - Duality Max-Flow/Min-cut. Primal Problem. x3. x7. x1. x4. x0. x0. max x0. st. x2. x5. x8. -x1-x2+x0 = 0. x6. x1-x3-x4 = 0. Max flow. x2-x5-x6 = 0. x3+x5-x7 = 0. x4+x6-x8 = 0.

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Next: Savage/Ellsberg/ Klibanoff et al./Snow

Foundations of Statistics Risk, Ambiguity, and the Savage Axioms A Smooth Model of Decision Making under Ambiguity On the possibility of profitable self-selection contracts in competitive insurance markets. Next: Savage/Ellsberg/ Klibanoff et al./Snow. Ellsberg Definitions.

303 views • 4 slides