A Single-phase Method for Quadratic Programming
Title | A Single-phase Method for Quadratic Programming PDF eBook |
Author | Stanford University. Systems Optimization Laboratory |
Publisher | |
Pages | 80 |
Release | 1986 |
Genre | |
ISBN |
This report describes a single-phase quadratic programming method, an active-set method which solves a sequence of equality-constraint quadratic programs.
A Single-phased Method for Quadratic Programming
Title | A Single-phased Method for Quadratic Programming PDF eBook |
Author | Stephen Carey Hoyle |
Publisher | |
Pages | 250 |
Release | 1985 |
Genre | |
ISBN |
Scientific and Technical Aerospace Reports
Title | Scientific and Technical Aerospace Reports PDF eBook |
Author | |
Publisher | |
Pages | 244 |
Release | 1991 |
Genre | Aeronautics |
ISBN |
Inertia-controlling Methods for Quadratic Programming
Title | Inertia-controlling Methods for Quadratic Programming PDF eBook |
Author | Philip E. Gill |
Publisher | |
Pages | 48 |
Release | 1988 |
Genre | Quadratic programming |
ISBN |
We also derive recurrance relations that facilitate the efficient implementation of a class of inertia-controlling methods that maintain the factorization of a nonsingular matrix associated with the Karush-Kuhn-Tucker conditions."
A Regularized Active-Set method For Sparse Convex Quadratic Programming
Title | A Regularized Active-Set method For Sparse Convex Quadratic Programming PDF eBook |
Author | |
Publisher | Stanford University |
Pages | 128 |
Release | |
Genre | |
ISBN |
Computation of Reliability and Shortage Distributions in Stochastic Transportation Networks with Cycles
Title | Computation of Reliability and Shortage Distributions in Stochastic Transportation Networks with Cycles PDF eBook |
Author | Stanford University. Department of Operations Research. Systems Optimization Laboratory |
Publisher | |
Pages | 998 |
Release | 1985 |
Genre | |
ISBN |
Practical Optimization
Title | Practical Optimization PDF eBook |
Author | Philip E. Gill |
Publisher | SIAM |
Pages | 421 |
Release | 2019-12-16 |
Genre | Mathematics |
ISBN | 1611975603 |
In the intervening years since this book was published in 1981, the field of optimization has been exceptionally lively. This fertility has involved not only progress in theory, but also faster numerical algorithms and extensions into unexpected or previously unknown areas such as semidefinite programming. Despite these changes, many of the important principles and much of the intuition can be found in this Classics version of Practical Optimization. This book provides model algorithms and pseudocode, useful tools for users who prefer to write their own code as well as for those who want to understand externally provided code. It presents algorithms in a step-by-step format, revealing the overall structure of the underlying procedures and thereby allowing a high-level perspective on the fundamental differences. And it contains a wealth of techniques and strategies that are well suited for optimization in the twenty-first century, and particularly in the now-flourishing fields of data science, “big data,” and machine learning. Practical Optimization is appropriate for advanced undergraduates, graduate students, and researchers interested in methods for solving optimization problems.