Results 31 to 40 of about 13,500,255 (277)
Learning regularized Gauss-Newton methods
, 2022 We consider variational networks for a class of nonlinear-ill-posed least squares inverse problems. These problems are addressed by regularized Gauss-Newton type optimization algorithms where the regularization is learned by a neural network.
Francesco Colibazzi +3 more
core
Positive Definiteness of Symmetric Rank 1 (H-Version) Update for Unconstrained Optimization
مجلة بغداد للعلوم, 2022 Several attempts have been made to modify the quasi-Newton condition in order to obtain rapid convergence with complete properties (symmetric and positive definite) of the inverse of Hessian matrix (second derivative of the objective function).
Saad Shakir Mahmood +2 more
doaj +1 more source
A new class of self-scaling for quasi-Newton method based on the quadratic model [PDF]
, 2021 Quasi-Newton method is an efficient method for solving unconstrained optimization problems. Self-scaling is one of the common approaches in the modification of the quasi-Newton method.
A. Hassan, Basim +5 more
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Quasi-Newton Methods for Solving Nonlinear Programming Problems [PDF]
Computer Science Journal of Moldova, 1996 In the present paper the problem of constrained equality optimization is reduced to sequential solving a series of problems of quadratic programming. The Hessian of the Lagrangian is approximated by a sequence of symmetric positive definite matrices. The
V.Moraru
doaj
Advanced Robotics Research, EarlyView.A codesign multiobjective optimization framework was developed to enhance the morphology and controller of a snake‐like robot driven by artificial muscles. It improved planar locomotion, agility, and power efficiency. The approach optimized link geometry and controller gains, revealing that shorter muscles near joints and longer linkages maximize ...
Ayla Valles, Mahdi Haghshenas‐Jaryani
wiley +1 more source
From Folding Mechanics to Robotic Function: A Unified Modeling Framework for Compliant Origami
Advanced Science, EarlyView.A geometry‐consistent DDG framework captures compliant origami deformation from rigid‐like folding to soft panel bending. By tuning crease and panel stiffness, bistable snap‐through responses transform into smooth monostable deformation, enabling programmable stability and robot‐scale locomotion.
Bohan Zhang +7 more
wiley +1 more source
Mathematics, 2022 In this paper, we follow a chronological development of gradient descent methods and its accelerated variants later on. We specifically emphasise some contemporary approaches within this research field. Accordingly, a constructive overview over the class
Vladimir Rakočević +1 more
doaj +1 more source
Quasi-Newton methods with provable efficiency guarantees
, 2022 Quasi-Newton methods are very popular in Optimization. They have a long, rich history, and perform extremely well for solving real-life problems. However, almost nothing is known about theoretical efficiency guarantees for these methods. The goal of this
Rodomanov, Anton
core
Active Oscillations of Elastic Membranes Arising from the Coupling with a Catalytic Reaction
Advanced Science, EarlyView.Coupling an elastic membrane to a catalytic reaction produces active oscillations that self‐sustain for hours: the membrane cyclically inflates and deflates as the reaction spontaneously activates and deactivates, without external power. Energy release is directly embodied in the oscillation dynamics, opening elasto‐chemical strategies for the physical
Alberto Comoretto +5 more
wiley +1 more source
Quasi-Newton methods for generalized equations
, 1979 Newton's method is a well known and often applied technique for computing a zero of a nonlinear function. Situations arise in which it is undesirable to evaluate, at each iteration, the derivative appearing in the Newton iteration formula. In these cases,
Josephy, Norman
core +2 more sources