Amanote Research

Amanote Research

  • Register
  • Sign In

Implementable Tensor Methods in Unconstrained Convex Optimization

Mathematical Programming, Series B- Germany

doi 10.1007/s10107-019-01449-1

Full Text

Abstract

Available in

Categories

MathematicsSoftware

Date

November 21, 2019

Authors

Yurii Nesterov

Publisher

Springer Science and Business Media LLC


Related search

Evaluation Complexity of Adaptive Cubic Regularization Methods for Convex Unconstrained Optimization

Optimization Methods and Software
ControlApplied MathematicsOptimizationSoftware
2012English

An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization

Abstract and Applied Analysis
Applied MathematicsAnalysis
2014English

Efficient Methods for Large-Scale Unconstrained Optimization

Large-Scale Nonlinear Optimization
2006English

Unconstrained Optimization

2013English

Exploiting Negative Curvature Directions in Linesearch Methods for Unconstrained Optimization

Optimization Methods and Software
ControlApplied MathematicsOptimizationSoftware
2000English

Unconstrained Optimization

2003English

Unconstrained Optimization

2004English

Unconstrained Optimization

2003English

New Adaptive Conjugate Gradient Methods Choices for Unconstrained Optimization

New Trends in Mathematical Science
2018English

Amanote Research

Note-taking for researchers

Follow Amanote


© 2026 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy