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How do you solve a linear programming problem with a graph?
How do you solve a linear programming problem with a graph?

Pdf - Text of NPTEL IIT Video Lectures
Pdf - Text of NPTEL IIT Video Lectures

... sequence x n is a bounded sequence, because x n lies between a and b, so that all the terms of the sequence have a lower bound say a, upper bound say b; it is a bounded sequence. And we know by Bolzano’s theorem, every bounded sequence has a convergence sequence. So, use the Bolzano-Weierstass theo ...
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When is a linear functional multiplicative? Krzysztof Jarosz

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Link-State Routing with Hop-by-Hop Forwarding Can Achieve Optimal Traffic Engineering Dahai Xu

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Enhanced form of solving real coded numerical optimization

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Solutions to Problems for Math. H90 Issued 19 Oct. 2007

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A Probabilistic Analysis for the Range Assignment - IIT-CNR

... minimizing the maximum of node transmitting ranges while achieving connectedness. They also considered the stronger requirement of bi-connectivity. They present centralized topology control algorithms that provide the optimal solution for both versions of the problem. The range assignment returned b ...
Single-Period Models (Discrete Demand)
Single-Period Models (Discrete Demand)

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Endogeneity and Sampling of Alternatives in Spatial Choice Models

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EFFICIENCY OF LOCAL SEARCH WITH

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An easy treatment of hanging nodes in hp-finite elements

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Understanding the concept of outlier and its relevance to the

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Recursive Bingham Filter for Directional Estimation Involving 180

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behavior based credit card fraud detection using

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THE PLANT LOCATION PROBLEM BY AN EXPANDED LINEAR

Download paper (PDF)
Download paper (PDF)

Reliable Space Pursuing for Reliability-based Design Optimization with Black-box Performance Functions
Reliable Space Pursuing for Reliability-based Design Optimization with Black-box Performance Functions

... meets the reliability requirement. In other words, the optimization process is constrained by the boundaries of the reliable design space. Then, the RBDO problem becomes a simple deterministic optimization problem constrained by the boundaries of RDS. Ref. [11] dealt with inexpensive performance fun ...
Contrast Functions for Blind Separation and Deconvolution of Sources
Contrast Functions for Blind Separation and Deconvolution of Sources

Solving Optimal Timing Problems Elegantly
Solving Optimal Timing Problems Elegantly

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Generalized linear model

In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression. They proposed an iteratively reweighted least squares method for maximum likelihood estimation of the model parameters. Maximum-likelihood estimation remains popular and is the default method on many statistical computing packages. Other approaches, including Bayesian approaches and least squares fits to variance stabilized responses, have been developed.
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