Algorithms for Linear-quadratic Optimization
β Scribed by Vasile Sima
- Publisher
- M. Dekker
- Year
- 1996
- Tongue
- English
- Leaves
- 374
- Series
- Monographs and textbooks in pure and applied mathematics 200
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This up-to-date reference offers valuable theoretical, algorithmic, and computational guidelines for solving the most frequently encountered linear-quadratic optimization problems - providing an overview of recent advances in control and systems theory, numerical linear algebra, numerical optimization, scientific computations, and software engineering. Examining state-of-the-art linear algebra algorithms and associated software, Algorithms for Linear-Quadratic Optimization presents algorithms in a concise, informal language that facilitates computer implementation...discusses the mathematical description, applicability, and limitations of particular solvers...summarizes numerical comparisons of various algorithms...highlights topics of current interest, including H[subscript infinity] and H[subscript 2] optimization, defect correction, and Schur and generalized-Schur vector methods...emphasizes structure-preserving techniques...contains many worked examples based on industrial models...covers fundamental issues in control and systems theory such as regulator and estimator design, state estimation, and robust control...and more. Furnishing valuable references to key sources in the literature, Algorithms for Linear-Quadratic Optimization is an incomparable reference for applied and industrial mathematicians, control engineers, computer programmers, electrical and electronics engineers, systems analysts, operations research specialists, researchers in automatic control and dynamic optimization, and graduate students in these disciplines.
β¦ Subjects
ΠΠ²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΡ;Π’Π΅ΠΎΡΠΈΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ (Π’ΠΠ£);ΠΠ½ΠΈΠ³ΠΈ Π½Π° ΠΈΠ½ΠΎΡΡΡΠ°Π½Π½ΡΡ ΡΠ·ΡΠΊΠ°Ρ ;
π SIMILAR VOLUMES
<p><P><EM>Optimization for Decision Making: Linear and Quadratic Models</EM> is a first-year graduate level text that illustrates how to formulate real world problems using linear and quadratic models; how to use efficient algorithms β both old and new β for solving these models; and how to draw use
<p><P><EM>Optimization for Decision Making: Linear and Quadratic Models</EM> is a first-year graduate level text that illustrates how to formulate real world problems using linear and quadratic models; how to use efficient algorithms β both old and new β for solving these models; and how to draw use
This book, developed through class instruction at MIT over the last 15 years, provides an accessible, concise, and intuitive presentation of algorithms for solving convex optimization problems. It relies on rigorous mathematical analysis, but also aims at an intuitive exposition that makes use of vi