Traditional approaches to analytical method optimization (e.g., univariate and “guess-and-check”) can be time-consuming, costly, and often fail to identify true optima within the parameter space.

Abstract: For the conjugate gradient method to solve the unconstrained optimization problem, given a new interval method to obtain the direction parameters, and a new conjugate gradient algorithm is ...

I'm exploring the possibility of contributing a collection of differentiable multi-objective optimization (MOO) test functions to the OptimizationProblems.jl repository. I have personally implemented ...

Abstract: Conjugate gradient techniques are widely used to solve unconstrained optimization issues. The accelerated conjugate gradient approach provides superior numerical effects for the ...

This study introduced an efficient method for solving non-linear equations. Our approach enhances the traditional spectral conjugate gradient parameter, resulting in significant improvements in the ...

Language-based agentic systems represent a breakthrough in artificial intelligence, allowing for the automation of tasks such as question-answering, programming, and advanced problem-solving. These ...

Gradient descent is a method to minimize an objective function F(θ) It’s like a “fitness tracker” for your model — it tells you how good or bad your model’’ predictions are. Gradient descent isn’t a ...

Adam is widely used in deep learning as an adaptive optimization algorithm, but it struggles with convergence unless the hyperparameter β2 is adjusted based on the specific problem. Attempts to fix ...

The present paper focuses on the optimization of large-flow coefficient centrifugal compressors, utilizing a mature centrifugal compressor impeller with a flow coefficient of 0.16 under design point ...