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Mastering Scientific Computing with R

You're reading from   Mastering Scientific Computing with R Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781783555253
Length 432 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Programming with R 2. Statistical Methods with R FREE CHAPTER 3. Linear Models 4. Nonlinear Methods 5. Linear Algebra 6. Principal Component Analysis and the Common Factor Model 7. Structural Equation Modeling and Confirmatory Factor Analysis 8. Simulations 9. Optimization 10. Advanced Data Management Index

Quadratic programming


Quadratic programming is an optimization problem where the objective function is quadratic and the constraint functions are linear. We can solve quadratic programs in R using the solve.QP() function part of the quadprog package. Quadratic programs are often expressed in the form of the following equation:

The preceding equation is subject to the following constraints:

However, the solve.QP() function requires that your quadratic program be written in the standard form:

The preceding equation is subject to the following constraint:

In the preceding constraint, T is the mathematical symbol for transpose (matrix A, but with its rows swapped for columns), is a vector of p unknowns, G is a positive definite symmetric matrix, d is a vector of length p, A is a matrix, and b is a vector with the length of the number of constraints c. For example, let's solve the following quadratic program:

The preceding equation is subject to the following constraints:

First, we install the...

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