24 papers · ranked by Valyu relevance
Authors not listed
Physics-based coarse-grained (CG) models are widely used in (bio)molecular simulations, yet their parameterization remains challenging and labor-intensive. In this work, we demonstrate how recently developed gradient-based optimization methods can substantially accelerate the refinement of CG force field (FF)…
Marius Pille, Leon Martin, Emilius Richter, Dionysios Perdikis + 2 more
Personalized brain modeling at clinically relevant scales requires integrating biophysical models with empirical neuroimaging data, yet high-dimensional parameter estimation in whole-brain network models remains computationally prohibitive. We present TVB-Optim, an open-source Python library providing a general and…
Grant Norman, Conor Rowan, Kurt Maute, Alireza Doostan
In this work, we investigate the use of data-driven equation discovery for dynamical systems to model and forecast continuous-time dynamics of unconstrained optimization problems. To avoid expensive evaluations of the objective function and its gradient, we leverage trajectory data on the optimization variables to…
MOHAMED HASSAN, ALEKSANDAR VAKANSKI, BOYU ZHANG, MIN XIAN
The generalization performance of deep neural networks (DNNs) is a critical factor in achieving robust model behavior on unseen data. Recent studies have highlighted the importance of sharpness-based measures in promoting generalization by encouraging convergence to flatter minima. Among these approaches…
Wang Zhi-feng, Li Long-Long, Zeng, Chunyan
Within the current sphere of deep learning research, despite the extensive application of optimization algorithms such as Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam), there remains a pronounced inadequacy in their capability to address fluctuations in learning efficiency, meet the demands of…
Mohamed A. Mokhtar, Mohamed Fathy, Yasser A. Dahab, Emad A. Sayed
In modern machine learning, optimization algorithms are crucial; they steer the training process by skillfully navigating through complex, high-dimensional loss landscapes. Among these, stochastic gradient descent with momentum (SGDM) is widely adopted for its ability to accelerate convergence in shallow regions.…
Surjanovic, Nikola, Bouchard-Côté, Alexandre + 2 more
The performance of gradient-based optimization methods, such as standard gradient descent (GD), greatly depends on the choice of learning rate. However, it can require a non-trivial amount of user tuning effort to select an appropriate learning rate schedule. When such methods appear as inner loops of other algorithms…
Kosuke Hamazaki, Hiroyoshi Iwata, Koji Tsuda, Russell Schwartz
In this study, we calculated gradients from AD through DiffBreed, a PyTorch-based differentiable breeding simulator. We then leveraged these gradients to optimize progeny allocation in breeding schemes using gradient-based methods. We explored the relationship between function values and gradients across various…
Ming Li
Gradient descent is the most commonly used optimization method, but limited to local optimality, and confined to the field of continuous differentiable problems with simple convex constraints. This work solve these limitations and restrictions by unifying all optimization problems with various complex constraints as a…
Steven A. Frank, Antonio M. Scarfone
Diverse learning algorithms, optimization methods, and natural selection share a common mathematical structure despite their apparent differences. Here, I show that a simple notational partitioning of change by the Price equation reveals a universal force-metric-bias (FMB) law: $Δθ=(Mf+b+ξ)$. The force $f$ drives…
Sinho Chewi
These lecture notes cover the theory of convex optimization, with a particular emphasis on first-order methods.
Yuhang Xie, Wei Li, Cheng Zhong, Shang Gao + 4 more
Given the growing complexity of continuous optimization problems in strongly coupled and black-box environments, this study proposes a novel adaptive gradient-guided metaheuristic, referred to as Self-Adaptive AdamW-Guided Optimization (SAWG). Without requiring explicit gradient information, SAWG constructs…
Lulu He, Yanan Du, Jianchao Bai
The conjugate gradient method is widely recognized as a foundational technique for large-scale unconstrained optimization. In this work, we introduce an Accelerated Stochastic Conjugate Gradient (ASCG) algorithm, specifically designed for a class of convex empirical risk minimization problems. The proposed ASCG method…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Odin Zhang, Jiaqi Wang, Tuscan Rock Thompson, Ziyi You + 3 more
Biomolecular interactions, including protein–protein interactions, protein–nucleic acid recognition, and protein–small molecule binding, underlie a wide range of biological processes and therapeutic mechanisms. Although recent de novo design methods can generate candidate binders for diverse molecular targets…
Simon Weissmann
These lecture notes provide an introduction to first-order optimization methods with a particular emphasis on stochastic gradient methods. We begin with deterministic gradient based methods for unconstrained optimization and study their convergence under standard assumptions such as smoothness, convexity, strong…
Ray Zirui Zhang, Christopher E. Miles, Xiaohui Xie, John S. Lowengrub
We propose a new neural network based method for solving inverse problems for partial differential equations (PDEs) by formulating the PDE inverse problem as a bilevel optimization problem. At the upper level, we minimize the data loss with respect to the PDE parameters. At the lower level, we train a neural network to…
Jesús García Fernández, Nasir Ahmad, Marcel van Gerven
Iterative optimization is central to modern artificial intelligence (AI) and provides a crucial framework for understanding adaptive systems. This review provides a unified perspective on this subject, bridging classic theory with neural network training and biological learning. Although gradient-based methods, powered…
Adam Siepel, Rebecca Hassett, Stephen J. Staklinski
Bayesian phylogenetic inference is now widely used but remains heavily reliant on Markov chain Monte Carlo (MCMC) sampling, which is computationally intensive and requires careful convergence monitoring. Variational inference (VI) is an appealing alternative that approximates posterior distributions without sampling…
Authors not listed
This work provides a rigorous theoretical investigation of selective error correction strategies for variational quantum algorithms, with focus on understanding the interplay between error suppression, circuit trainability, and computational resource requirements. We develop a mathematical framework that characterizes…
Authors not listed
Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…
Authors not listed
The GENERIC framework provides a robust structure for nonequilibrium dynamics but lacks a principled method to select reversible ($L$) and irreversible ($M$) brackets. Similarly, finite-time optimizations minimizing path-averaged reciprocal temperature exist but remain isolated. Here, we introduce the \textbf{Entropy…
Authors not listed
Meta-GGA density functional theory (DFT) is an important method in ab initio materials modelling; however, its computational cost limits applicability for generating large datasets or simulating extended length and time scales, as necessary for modern materials discovery. Deorbitalization is a promising strategy to…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…