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Quadratic Programming with Computer Programs

Medium: Buch
ISBN: 978-1-4987-3575-9
Verlag: Taylor & Francis Inc
Erscheinungstermin: 18.01.2017
Lieferfrist: bis zu 10 Tage

Quadratic programming is a mathematical technique that allows for the optimization of a quadratic function in several variables. QP is a subset of Operations Research and is the next higher lever of sophistication than Linear Programming. It is a key mathematical tool in Portfolio Optimization and structural plasticity. This is useful in Civil Engineering as well as Statistics.


Produkteigenschaften


  • Artikelnummer: 9781498735759
  • Medium: Buch
  • ISBN: 978-1-4987-3575-9
  • Verlag: Taylor & Francis Inc
  • Erscheinungstermin: 18.01.2017
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2017
  • Serie: Advances in Applied Mathematics
  • Produktform: Gebunden
  • Gewicht: 868 g
  • Seiten: 400
  • Format (B x H x T): 263 x 190 x 26 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

Geometrical Examples

Geometry of a QP: Examples

Geometrical Examples

Optimality Conditions

Geometry of Quadratic Functions

Nonconvex QP’s

Portfolio Opimization

The Efficient Frontier

The Capital Market Line

QP Subject to Linear Equality Constraints

QP Preliminaries

QP Unconstrained: Theory

QP Unconstrained: Algorithm 1

QP with Linear Equality Constraints: Theory

QP with Linear Equality Constraints: Alg. 2

Quadratic Programming

QP Optimality Conditions

QP Duality

Unique and Alternate Optimal Solutions

Sensitivity Analysis

QP Solution Algorithms

A Basic QP Algorithm: Algorithm 3

Determination of an Initial Feasible Point

An Efficient QP Algorithm: Algorithm 4

Degeneracy and Its Resolution

A Dual QP Algorithm

Algorithm 5

General QP and Parametric QP Algorithms

A General QP Algorithm: Algorithm 6

A General Parametric QP Algorithm: Algorithm 7

Symmetric Matrix Updates

Simplex Method for QP and PQP

Simplex Method for QP: Algorithm 8

Simplex Method for Parametric QP: Algorithm 9

Nonconvex Quadratic Programming

Optimality Conditions

Finding a Strong Local Minimum: Algorithm 10