Search Results - subject:"Unconstrained minimization"

Tensor methods for minimizing convex functions with Hölder continuous higher-order derivatives

Grapiglia, Geovani Nunes; Nesterov, Jurij Evgenʹevič - 2019

Persistent link: https://www.econbiz.de/10012215207

Tensor methods for finding approximate stationary points of convex functions

Grapiglia, Geovani Nunes; Nesterov, Jurij Evgenʹevič - 2019

Persistent link: https://www.econbiz.de/10012215211

On inexact solution of auxiliary problems in tensor methods for convex optimization

Grapiglia, Geovani Nunes; Nesterov, Jurij Evgenʹevič - 2019

Persistent link: https://www.econbiz.de/10012215218

Accelerated regularized Newton methods for minimizing composite convex functions

Grapiglia, Geovani N.; Nesterov, Jurij Evgenʹevič - 2018

Persistent link: https://www.econbiz.de/10011992656

Globally convergent second-order schemes for minimizing twice-differentiable functions

Nesterov, Jurij Evgenʹevič; Grapiglia, Geovani Nunes - 2016

Persistent link: https://www.econbiz.de/10011893984

Algebraic rules for quadratic regularization of Newton’s method

Karas, Elizabeth; Santos, Sandra; Svaiter, Benar - In: Computational Optimization and Applications 60 (2015) 2, pp. 343-376

In this work we propose a class of quasi-Newton methods to minimize a twice differentiable function with Lipschitz continuous Hessian. These methods are based on the quadratic regularization of Newton’s method, with algebraic explicit rules for computing the regularizing parameter. The...

Persistent link: https://www.econbiz.de/10011241274

Unconstrained weibit stochastic user equilibrium model with extensions

Kitthamkesorn, Songyot; Chen, Anthony - In: Transportation Research Part B: Methodological 59 (2014) C, pp. 1-21

This study provides an unconstrained minimization program as an alternative formulation for the multinomial weibit (MNW … different trip lengths under congested conditions. Qualitative properties of the unconstrained minimization program are given to … establish the equivalency and uniqueness of the MNW-SUE solution. The advantage of the unconstrained minimization programming …

Persistent link: https://www.econbiz.de/10010738262

Accelerating the cubic regularization of Newton’s method on convex problems

NESTEROV, Yu. - Center for Operations Research and Econometrics (CORE), … - 2005

In this paper we propose an accelerated version of the cubic regularization of Newton's method [6]. The original version, used for minimizing a convex function with Lipschitz-continuous Hessian, guarantees a global rate of convergence of order O(1/k exp.2), where k is the iteration counter. Our...

Persistent link: https://www.econbiz.de/10005065351

On the convergence of trust region algorithms for unconstrained minimization without derivatives

Powell, M. - In: Computational Optimization and Applications 53 (2012) 2, pp. 527-555

We consider iterative trust region algorithms for the unconstrained minimization of an objective function …

Persistent link: https://www.econbiz.de/10010998246