• Publications
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Introductory Lectures on Convex Optimization - A Basic Course
  • Y. Nesterov
  • Computer Science
  • Applied Optimization
  • 9 April 2014
TLDR
It was in the middle of the 1980s, when the seminal paper by Kar markar opened a new epoch in nonlinear optimization. Expand
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  • 633
Smooth minimization of non-smooth functions
  • Y. Nesterov
  • Mathematics, Computer Science
  • Math. Program.
  • 1 May 2005
TLDR
We propose a new approach for constructing efficient gradient schemes for non-smooth convex optimization, based on a special smoothing technique. Expand
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  • 344
  • PDF
Interior-point polynomial algorithms in convex programming
TLDR
In this book, the authors describe the first unified theory of polynomial-time interior point methods. Expand
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  • 271
  • PDF
Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems
  • Y. Nesterov
  • Mathematics, Computer Science
  • SIAM J. Optim.
  • 24 April 2012
TLDR
In this paper we propose new methods for solving huge-scale optimization problems based on random partial update of decision variables. Expand
  • 1,028
  • 173
  • PDF
Gradient methods for minimizing composite functions
  • Y. Nesterov
  • Mathematics, Computer Science
  • Math. Program.
  • 2013
TLDR
We analyze several new methods for solving optimization problems with the objective function formed as a sum of two terms: one is smooth and given by a black-box oracle, and another is a simple general convex function with known structure. Expand
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  • 109
  • PDF
Cubic regularization of Newton method and its global performance
TLDR
We provide theoretical analysis for a cubic regularization of Newton method as applied to unconstrained minimization problem. Expand
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  • 102
  • PDF
Random Gradient-Free Minimization of Convex Functions
TLDR
In this paper, we prove new complexity bounds for methods of convex optimization based only on computation of the function value. Expand
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  • 96
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Primal-dual subgradient methods for convex problems
  • Y. Nesterov
  • Mathematics, Computer Science
  • Math. Program.
  • 7 April 2009
TLDR
We present the variants of subgradient schemes for nonsmooth convex minimization, minimax problems, saddle point problems, variational inequalities and stochastic optimization. Expand
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  • 89
  • PDF
Self-Scaled Barriers and Interior-Point Methods for Convex Programming
TLDR
This paper provides a theoretical foundation for efficient interior-point algorithms for convex programming problems expressed in conic form, when the cone and its associated barrier are self-scaled. Expand
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  • 85
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