Search Results - subject:"Gradient method"

A structured Fletcher-Revees spectral conjugate gradient method for unconstrained optimization with application in robotic model

Salihu, Nasiru; Kumam, Poom; Awwal, Aliyu Muhammed; … - In: Operations research forum 4 (2023) 4, pp. 1-25

Persistent link: https://www.econbiz.de/10014388676

Convergence rate analysis of the multiplicative gradient method for PET-type problems

Zhao, Renbo - In: Operations research letters 51 (2023) 1, pp. 26-32

Persistent link: https://www.econbiz.de/10014283282

Exact gradient methods with memory

Florea, Mihai I. - 2019

Persistent link: https://www.econbiz.de/10012215179

Optimal scaling parameters for spectral conjugate gradient methods

Fahs, Amin; Fahs, Hassane; Dehghani, R. - In: Operations research forum 3 (2022) 2, pp. 1-13

Persistent link: https://www.econbiz.de/10013277253

A new augmented lagrangian method for MPCCs-theoretical and numerical comparison with existing augmented Lagrangian methods

Guo, Lei; Deng, Zhibin - In: Mathematics of operations research 47 (2022) 2, pp. 1229-1246

Persistent link: https://www.econbiz.de/10013365285

A spectral three-term Hestenes-Stiefel conjugate gradient method

Faramarzi, Parvaneh; Amini, Keyvan - In: 4OR : quarterly journal of the Belgian, French and … 19 (2021) 1, pp. 71-92

Persistent link: https://www.econbiz.de/10012495031

On the worst-case complexity of the gradient method with exact line search for smooth strongly convex functions

Klerk, Etienne de; Glineur, François; Taylor, Adrien B. - 2016

Persistent link: https://www.econbiz.de/10011893983

A new proposal to improve the early iterations in the interior point method

Heredia, Manolo Rodriguez; Oliveira, Aurelio Ribeiro Leite - 2020

Persistent link: https://www.econbiz.de/10012233492

Fast proximal algorithms for nonsmooth convex optimization

Ouorou, Adam - In: Operations research letters 48 (2020) 6, pp. 777-783

Persistent link: https://www.econbiz.de/10012430131

Complexity bounds for primal-dual methods minimizing the model of objective function

NESTEROV, Yurii - Center for Operations Research and Econometrics (CORE), … - 2015

We provide Frank-Wolfe (= Conditional Gradients) method with a convergence analysis allowing to approach a primal-dual solution of convex optimization problem with composite objective function. Additional properties of complementary part of the objective (strong convexity) significantly...

Persistent link: https://www.econbiz.de/10011246288