<p>It was in the middle of the 1980s, when the seminal paper by KarΒ markar opened a new epoch in nonlinear optimization. The importance of this paper, containing a new polynomial-time algorithm for linear opΒ timization problems, was not only in its complexity bound. At that time, the most surprisi
Introductory Lectures on Convex Programming Volume I: Basic course
β Scribed by Nesterov Yu.
- Tongue
- English
- Leaves
- 212
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Boston, Kluwer, 2004, 212Ρ.
Nonlinear Programming.The World of Nonlinear Optimization.
Local methods in unconstrained minimization.
First-order methods in nonlinear optimization.
Smooth Convex Programming.
Minimization of Smooth Functions.
Optimal Methods.
Minimization Problem with Smooth Components.
Nonsmooth Convex Programming.
General Convex Functions.
Nonsmooth Minimization Methods.
Methods with Complete Data.
Structural Programming.
Self-Concordant Functions.
Self-Concordant Barriers.
Applications of Structural Programming.
β¦ Subjects
ΠΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠ°;ΠΠ΅ΡΠΎΠ΄Ρ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ
π SIMILAR VOLUMES
Nesterov (Center of Operations Research and Econometrics, Universitè Catholique de Louvain, Belgium) explains the main ideas of complexity theory for convex optimization, covering optimal methods and lower complexity bounds for smooth and non-smooth convex optimization. A separate chapter is devoted
<p><span>This book provides easy access to the basic principles and methods for solving constrained and unconstrained convex optimization problems. Included are sections that cover: basic methods for solving constrained and unconstrained optimization problems with differentiable objective functions;
Part 1 begins with an overview of properties of the real numbers and starts to introduce the notions of set theory. The absolute value and in particular inequalities are considered in great detail before functions and their basic properties are handled. From this the authors move to differential and