Results 71 to 80 of about 97,762 (292)

Representing Symmetric Rank Two Updates [PDF]

open access: yes
Various quasi-Newton methods periodically add a symmetric "correction" matrix of rank at most 2 to a matrix approximating some quantity A of interest (such as the Hessian of an objective function).
David M. Gay
core  

Weak‐Story Mitigation and Drift Redistribution in Moment‐Resisting Frames Using Stepping Rocking Walls

open access: yesEarthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT This study investigates the seismic response of moment‐resisting frames retrofitted with a stepping rocking wall, with emphasis on weak‐story mitigation and residual drift reduction under both ordinary and pulse‐like ground motions. Nonlinear response‐history analyses are conducted on 9‐story and 20‐story SAC Los Angeles frames.
Mehrdad Aghagholizadeh
wiley   +1 more source

Numerical Experience with Damped Quasi-Newton Optimization Methods when the Objective Function is Quadratic

open access: yesSultan Qaboos University Journal for Science, 2012
A class of damped quasi-Newton methods for nonlinear optimization has recently been proposed by extending the damped-technique of Powell for the BFGS method to the Broyden family of quasi-Newton methods.
Mehiddin Al-Baali, Anton Purnama
doaj   +1 more source

A quasi-Newton proximal splitting method

open access: yes, 2012
A new result in convex analysis on the calculation of proximity operators in certain scaled norms is derived. We describe efficient implementations of the proximity calculation for a useful class of functions; the implementations exploit the piece-wise linear nature of the dual problem.
Becker, Stephen, Fadili, Jalal M.
openaire   +4 more sources

Applied Element Modelling of Out‐Of‐Plane Instability in Boundary Elements of Thin RC Walls

open access: yesEarthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Recent earthquakes in Chile (2010) and New Zealand (2011) caused extensive damage to buildings featuring reinforced concrete (RC) structural walls, with observed failure modes including out‐of‐plane instability of slender walls. This buckling‐type response under combined in‐plane and out‐of‐plane loading has also been documented in ...
Andrea Orgnoni   +2 more
wiley   +1 more source

Stability Evaluation and Parametric Optimization of Coal‐Concrete Composite Bearing Systems Under Mine‐Water‐Induced Deterioration: Experiments and FEINN Analysis

open access: yesEnergy Science &Engineering, EarlyView.
Mine‐water immersion tests reveal pronounced coal weakening (vs. minor concrete degradation), identifying coal pillars as the stability‐limiting component in composite dams. A coupled FEINN framework quantifies extreme‐pressure stability and ranks multi‐parameter designs via a normalized multi‐indicator scheme, enabling optimized dam configuration for ...
He Wen   +6 more
wiley   +1 more source

Quasi-Newton methods for solving non-smooth multiobjective optimization

open access: yesJournal of Mathematical Extension, 2014
In this paper, a quasi-Newton type algorithm for non-smooth multiobjective optimization is presented. In this algorithm, in every iteration a quadratic subproblem solves until a critical point is reached. Moreover, the global convergence of the algorithm
Najmeh Hoseini Monjezi
doaj  

An Efficient Limited Memory Multi-Step Quasi-Newton Method

open access: yesMathematics
This paper is dedicated to the development of a novel class of quasi-Newton techniques tailored to address computational challenges posed by memory constraints. Such methodologies are commonly referred to as “limited” memory methods.
Issam A. R. Moghrabi, Basim A. Hassan
doaj   +1 more source

Denoising of ASL Data Using Deep Learning Priors Generated From Distribution Remapping

open access: yesMagnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop an effective deep learning (DL)–based method to denoise arterial spin labeling (ASL) data. Methods Conventional DL–based ASL denoising methods often suffer from overfitting and poor generalization when training data are limited.
Ziyang Xu   +9 more
wiley   +1 more source

Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods

open access: yes, 2022
Optimization is important in machine learning problems, and quasi-Newton methods have a reputation as the most efficient numerical schemes for smooth unconstrained optimization.
Lin, Dachao, Ye, Haishan, Zhang, Zhihua
core  

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