We propose using Lévy α-stable distributions to construct priors for Bayesian inverse
problems. The construction is based on Markov fields with stable-distributed increments …

Abstract Backpropagation Neural Network (BPNN) is an artificial intelligence technique that
has seen several applications in many fields of science and engineering. It is well-known …

A new three-term conjugate gradient-based projection method is presented in this paper for
solving large-scale nonlinear monotone equations. This method is derivative-free and it is …

Three hybrid methods for solving unconstrained optimization problems are introduced.
These methods are defined using proper combinations of the search directions and included …

The quasi-Newton is a well-known method for solving small to medium-scale unconstrained
optimization problems due to its simplicity and convergence. This leads to many …

This paper proposed an effective conjugate gradient parameter via the hybridization of quasi-
Newton and conjugate gradient search directions. The method gives a sufficient descent …

In this paper, we present some hybrid methods for solving unconstrained optimization
problems. These methods are defined using proper combinations of the search directions …

Despite the global fast coarse search capability of Teaching-Learning Based Optimization
(TLBO), analysis in literature on the performance of TLBO reveals it often risks getting …

This thesis investigates quantum algorithms for eigenstate preparation, with a primary focus
on solving eigenvalue problems such as the Schrödinger equation by utilizing near-term …

A new conjugate gradient coefficient was derived in the field of constrained optimization
based on the objective and constraint function using the interior pointe method and taking …