Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become central to scientific progress.

This paper presents the Secant Optimization Algorithm (SOA), a novel mathematics-inspired metaheuristic derived from the Secant Method. SOA enhances search efficiency by repeating vector updates using ...

Abstract: Optimization problems involving the minimization of a finite sum of smooth, possibly non-convex functions arise in numerous applications. To achieve a consensus solution over a network, ...

High-performance optimization algorithms are essential in deep learning. However, understanding the behavior of optimization (i.e., learning process) remains challenging due to the instability and ...

Once upon a time, in the magical land of Machine Learning, there lived two hikers named Connie and Nonnie. They were best friends but had very different approaches to hiking. Their adventures on two ...

Abstract: Decentralized optimization strategies are helpful for various applications, from networked estimation to distributed machine learning. This paper studies finite-sum minimization problems ...

Computer-generated holography (CGH) provides an approach to digitally modulate a given wavefront. This technology, partly inherited from optical holography and partly advanced by the progress of ...

They discussed methods for creating holograms using alternative projections and gradient descent. The goal was to help improve hologram generation and showcase recent progress in this quickly evolving ...

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Numerous real-world issues can be modelled as convex optimisation problems, making convex optimisation a useful technique in machine learning. Finding the optimal hyperplane to divide the data points ...

1 Department of Mechanical Engineering, Toyo University, Kawagoe, Japan. 2 Graduate School of Science and Engineering, Toyo University, Kawagoe, Japan. In this study, we estimate the drag force acting ...