My graduate studies included learning about constraint-based optimization algorithms (such as linear programming) and ...

Introduction: In unsupervised learning, data clustering is essential. However, many current algorithms have issues like early convergence, inadequate local search capabilities, and trouble processing ...

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.

Change is the only constant in today’s rapidly evolving digital marketing landscape. Keeping up with the latest innovations isn’t just a choice – it’s a necessity for survival. Generative engine ...

Group Relative Policy Optimization (GRPO) is a novel reinforcement learning method introduced in the DeepSeekMath paper earlier this year. GRPO builds upon the Proximal Policy Optimization (PPO) ...

Multitask learning (MLT) involves training a single model to perform multiple tasks simultaneously, leveraging shared information to enhance performance. While beneficial, MLT poses challenges in ...

ABSTRACT: This study adopts the Dantzig’s Simplex method to investigate optimization of sand casting parameters for optimum service performance. Some process variables and mechanical properties were ...

Web Page code (HTML, CSS, and JavaScript) responsible for an application that, in the context of linear optimization, gives the best solution through the Simplex Method. Made alongside Paulo Henrique ...