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Two approaches are known for solving large-scale unconstrained optimization problems—the limited-memory quasi-Newton method (truncated Newton method) and the conjugate gradient method. This is the first book to detail conjugate gradient methods, showing their properties and converge...
Two approaches are known for solving large-scale unconstrained optimization problems—the limited-memory quasi-Newton method (truncated Newton method) and the conjugate gradient method. This is the first book to detail conjugate gradient methods, showing their properties and converge...
Two approaches are known for solving large-scale unconstrained optimization problems—the limited-memory quasi-Newton method (truncated Newton method) and the conjugate gradient method. This is the first book to detail conjugate gradient methods, showing their properties and converge...
The position taken in this collection of pedagogically written essays is that conjugate gradient algorithms and finite element methods complement each other extremely well.
Via their combinations practitioners have been able to solve complicated, direct and inverse, multidemensional problems modele...
This textbook provides an introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimization and regression-based methods using a singu...
This book includes a thorough theoretical and computational analysis of unconstrained and constrained optimization algorithms and combines and integrates the most recent techniques and advanced computational linear algebra methods. Nonlinear optimization methods and techniques have reached their mat...
This second edition of the book includes a thorough theoretical and computational analysis of unconstrained and constrained optimization algorithms. The qualifier modern in the title refers to the unconstrained and constrained optimization algorithms that combine and integrate the latest and the mos...
This textbook provides and introduction to numerical computing and its applications in science and engineering.  The topics covered include those usually found in an introductory course, as well as those that arise in data analysis.  This includes optimization and regression based methods ...
This book provides a better clue to apply quantum derivative instead of classical derivative in the modified optimization methods, compared with the competing books which employ a number of standard derivative optimization techniques to address large-scale, unconstrained optimization issues. Essenti...
This graduate-level textbook introduces the reader to the area of inverse problems, vital to many fields including geophysical exploration, system identification, nondestructive testing, and ultrasonic tomography. It aims to expose the basic notions and difficulties encountered with ill-posed proble...
The position taken in this collection of pedagogically written essays is that conjugate gradient algorithms and finite element methods complement each other extremely well.
Via their combinations practitioners have been able to solve complicated, direct and inverse, multidemensional problems modele...
The position taken in this collection of pedagogically written essays is that conjugate gradient algorithms and finite element methods complement each other extremely well.
Via their combinations practitioners have been able to solve complicated, direct and inverse, multidemensional problems modele...