Results 31 to 40 of about 19,980 (297)

Gravitational Co-evolution and Opposition-based Optimization Algorithm [PDF]

International Journal of Computational Intelligence Systems, 2013
In this paper, a Gravitational Co-evolution and Opposition-based Optimization (GCOO) algorithm is proposed for solving unconstrained optimization problems.
Yang Lou, Junli Li, Yuhui Shi, Linpeng Jin   +3 more
doaj   +1 more source

A Nonmonotone Weighting Self-Adaptive Trust Region Algorithm for Unconstrained Nonconvex Optimization

Discrete Dynamics in Nature and Society, 2015
A new trust region method is presented, which combines nonmonotone line search technique, a self-adaptive update rule for the trust region radius, and the weighting technique for the ratio between the actual reduction and the predicted reduction.
Yunlong Lu, Weiwei Yang, Wenyu Li, Xiaowei Jiang, Yueting Yang   +4 more
doaj   +1 more source

Some algorithms for classes of split feasibility problems involving paramonotone equilibria and convex optimization

Journal of Inequalities and Applications, 2019
In this paper, we first introduce a new algorithm which involves projecting each iteration to solve a split feasibility problem with paramonotone equilibria and using unconstrained convex optimization.
Q. L. Dong, X. H. Li, D. Kitkuan, Y. J. Cho, P. Kumam   +4 more
doaj   +1 more source

A Novel Self-Adaptive Trust Region Algorithm for Unconstrained Optimization

Journal of Applied Mathematics, 2014
A new self-adaptive rule of trust region radius is introduced, which is given by a piecewise function on the ratio between the actual and predicted reductions of the objective function.
Yunlong Lu, Wenyu Li, Mingyuan Cao, Yueting Yang   +3 more
doaj   +1 more source

An accelerated first-order method for solving SOS relaxations of unconstrained polynomial optimization problems [PDF]

, 2011
Our interest lies in solving sum of squares (SOS) relaxations of large-scale unconstrained polynomial optimization problems. Because interior-point methods for solving these problems are severely limited by the large-scale, we are motivated to explore ...
Sun, Xu Andy, Freund, Robert Michael, Bertsimas, Dimitris J.   +2 more
core   +1 more source

Nonlinear conjugate gradient methods for unconstrained optimization [PDF]

, 2020
Two approaches are known for solving large-scale unconstrained optimization problems—the limited-memory quasi-Newton method (truncated Newton method) and the conjugate gradient method.
Andrei, Neculai
core   +1 more source

Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif, Diane Demailly, Gabriella A. Horvàth, Juan Dario Ortigoza Escobar, Nathalie Dorison, Mayté Castro Jiménez, Cécile A. Hubsch, Thomas Wirth, Gun‐Marie Hariz, Sophie Huby, Morgan Dornadic, Zohra Souei, Muhammad Mushhood Ur Rehman, Simone Hemm, Mehdi Boulaymen, Eduardo M. Moraud, Jocelyne Bloch, Xavier Vasques   +17 more
wiley   +1 more source

A THREE-TERM CONJUGATE GRADIENT METHOD FOR LARGE-SCALE MINIMIZATION IN ARTIFICIAL NEURAL NETWORKS

Barekeng
Conjugate Gradient (CG) methods are widely used for solving unconstrained optimization problems due to their efficiency and low memory requirements. However, standard CG methods may not always guarantee sufficient descent condition, which can impact ...
Umar A Omesa, Muhammad Y. Waziri, Issam A. R. Moghrabi, Sulaiman M. Ibrahim, Gudu E B, Fakai S L, Rabiu Bashir Yunus, Elissa Nadia Madi   +7 more
doaj   +1 more source

The Diagonal Update for Unconstrained Optimization [PDF]

مجلة التربية والعلم, 2012
In this research we introduced a new update of the Hessian matrix or we updating only the diagonal elements of Hessian matrix, and make the non-diagonal elements always equal to zero and in this case we can preserve the sparse property so called the ...
Saad Shakir Mahmood, Ali Ibrahim Mansour, Haydir Ali Hassan   +2 more
doaj   +1 more source

Foundational Machine‐Learning Interatomic Potential for Simulating Chemically Complex Ni‐Based Superalloys

Advanced Engineering Materials, EarlyView.
We apply a foundational machine‐learning interatomic potential based on the graph atomic cluster expansion (GRACE) to simulate the commercial Ni‐based single‐crystal superalloy CMSX‐4. Hybrid Monte‐Carlo/molecular dynamics sampling resolves short‐range order in the γ phase and L12 sublattice occupancies in the γ’ phase and connects them to stacking ...
Aditya Vishwakarma, Sarath Menon, Fritz Körmann, Thomas Hammerschmidt, Ralf Drautz   +4 more
wiley   +1 more source