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Research Paper#Quantum Chemistry, Optimization🔬 ResearchAnalyzed: Jan 3, 2026 09:24

Derivative-Free Optimization for Quantum Chemistry

Published:Dec 30, 2025 23:15
1 min read
ArXiv

Analysis

This paper investigates the application of derivative-free optimization algorithms to minimize Hartree-Fock-Roothaan energy functionals, a crucial problem in quantum chemistry. The study's significance lies in its exploration of methods that don't require analytic derivatives, which are often unavailable for complex orbital types. The use of noninteger Slater-type orbitals and the focus on challenging atomic configurations (He, Be) highlight the practical relevance of the research. The benchmarking against the Powell singular function adds rigor to the evaluation.
Reference

“The study focuses on atomic calculations employing noninteger Slater-type orbitals. Analytic derivatives of the energy functional are not readily available for these orbitals.”

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Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:22

Evolution Strategies

Published:Sep 5, 2019 00:00
1 min read
Lil'Log

Analysis

The article introduces black-box optimization algorithms as alternatives to stochastic gradient descent for optimizing deep learning models. It highlights the scenario where the target function's analytic form is unknown, making gradient-based methods infeasible. The article mentions examples like Simulated Annealing, Hill Climbing, and Nelder-Mead method, providing a basic overview of the topic.
Reference

“Stochastic gradient descent is a universal choice for optimizing deep learning models. However, it is not the only option. With black-box optimization algorithms, you can evaluate a target function $f(x): \mathbb{R}^n \to \mathbb{R}$, even when you don’t know the precise analytic form of $f(x)$ and thus cannot compute gradients or the Hessian matrix.”

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