2025/05/07 by Sridhar, Nikhil, Shah, Sajiv
Computer Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Advanced Optimization Algorithms Research #FOS: Computer and information sciences #FOS: Mathematics #Mathematical Software (cs.MS) #Optimization and Control (math.OC) #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2505.04137
openalex publication_date 2025/05/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce optHIM, an open-source library of continuous unconstrained optimization algorithms implemented in PyTorch for both CPU and GPU. By leveraging PyTorch's autograd, optHIM seamlessly integrates function, gradient, and Hessian information into flexible line-search and trust-region methods. We evaluate eleven state-of-the-art variants on benchmark problems spanning convex and non-convex landscapes. Through a suite of quantitative metrics and qualitative analyses, we demonstrate each method's strengths and trade-offs. optHIM aims to democratize advanced optimization by providing a transparent, extensible, and efficient framework for research and education.