26 papers · ranked by Valyu relevance
Nir Shlezinger, Santiago Segarra, Yi Zhang, Dvir Avrahami + 3 more
Optimization methods play a central role in signal processing, serving as the mathematical foundation for inference, estimation, and control. While classical iterative optimization algorithms provide interpretability and theoretical guarantees, they often rely on surrogate objectives, require careful hyperparameter…
Quoc Tuan Nguyen Diep, Hoang Nhut Huynh, Thanh Ven Huynh, Minh Quan Cao Dinh + 2 more
'Minh Quan Cao Dinh' 'Anh Tu Tran' 'Trung Nghia Tran'] Title: Abstract Electrical Impedance Tomography (EIT) is a non-invasive method for imaging conductivity distributions within a target area. The inverse problem associated with EIT is nonlinear and ill-posed, leading to low spatial resolution reconstructions.…
Luca Blum, Mohamed Elgendi, Carlo Menon
This paper studied the effects of applying the Box-Cox transformation for classification tasks. Different optimization strategies were evaluated, and the results were promising on four synthetic datasets and two real-world datasets. A consistent improvement in accuracy was demonstrated using a grid exploration with…
Eric Hermes, Khachik Sargsyan, Habib Najm, Judit Zádor
We present a new algorithm for the optimization of molecular structures to saddle points on the potential energy surface using a redundant internal coordinate system. This algorithm automates the procedure of defining the internal coordinate system, including the handling of linear bending angles, e.g. through the…
Demelas, Francesca, Roux, Joseph Le + 8 more
This paper presents Bundle Network, a learning-based algorithm inspired by the Bundle Method for convex non-smooth minimization problems. Unlike classical approaches that rely on heuristic tuning of a regularization parameter, our method automatically learns to adjust it from data. Furthermore, we replace the iterative…
Harini Narayanan, Joshua Hinckley, Rachel Barry, Brendan Dang + 4 more
Optimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we have applied a Bayesian Optimization-based iterative framework for experimental design to accelerate cell culture media development for two applications. First, we show this…
Authors not listed
This paper presents a simplified model of iterative compound optimization in drug/agrochemical discovery. Compounds are represented as binary strings, with project evolution simulated through random bit changes. The model reproduces key statistical features of real projects, including activity distributions and…
Jason Rader, Terry Lyons, Patrick Kidger
We introduce Optimistix: a nonlinear optimisation library built in JAX and Equinox. Optimistix introduces a novel, modular approach for its minimisers and least-squares solvers. This modularity relies on new practical abstractions for optimisation which we call search and descent, and which generalise classical notions…
Harini Narayanan, Joshua A. Hinckley, Rachel Barry, Brendan Dang + 4 more
'Lenna A. Wolffe' 'Adel Atari' 'Yuen-Yi Tseng' 'J. Christopher Love'] Optimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we apply a Bayesian Optimization-based iterative framework for experimental design to accelerate cell culture…
Meng Jiao, Feng Liu
Electroencephalography (EEG)/Magnetoencephalography (MEG) source imaging aims to seek an estimation of underlying activated brain sources to explain the observed EEG/MEG recording. Due to the ill-posed nature of inverse problem, solving EEG/MEG Source Imaging (ESI) requires design of regularization or prior terms to…
Ruichen Jiang, Ali Kavis, Qiujiang Jin, Sujay Sanghavi + 1 more
Optimization Authors: ['Ruichen Jiang' 'Ali Kavis' 'Qiujiang Jin' 'Sujay Sanghavi' 'Aryan Mokhtari'] We propose adaptive, line search-free second-order methods with optimal rate of convergence for solving convex-concave min-max problems. By means of an adaptive step size, our algorithms feature a simple update rule…
José E. Cruz Serrallés, Ilias I. Giannakopoulos, Siqi Wang, Damien Chen + 4 more
The radiative characteristics of the radiofrequency receive coils dictate the signal-to-noise ratio (SNR) of magnetic resonance images. Despite the crucial importance of RF coils, the practical coil design process has remained a largely empirical one. This work introduces a novel optimization framework for rational…
Nikita Belokonev, Artem Melnikov, Maninadh Podapaka, Karan Pinto + 2 more
'Markus Pflitsch' 'Michael Perelshtein'] Chemical component design is a computationally challenging procedure that often entails iterative numerical modeling and authentic experimental testing. We demonstrate a novel optimization method, Tensor train Optimization (TetraOpt), for the shape optimization of components…
Yifan Yang, Hao Ban, Minhui Huang, Shiqian Ma + 1 more
Analysis Authors: ['Yifan Yang' 'Hao Ban' 'Minhui Huang' 'Shiqian Ma' 'Kaiyi Ji'] Bilevel optimization has recently attracted considerable attention due to its abundant applications in machine learning problems. However, existing methods rely on prior knowledge of problem parameters to determine stepsizes, resulting in…
Lippl Samuel, Peters Benjamin, Kriegeskorte Nikolaus
Recent work has suggested that feedforward residual neural networks (ResNets) approximate iterative recurrent computations. Iterative computations are useful in many domains, so they might provide good solutions for neural networks to learn. Here we quantify the degree to which ResNets learn iterative solutions and…
Akhil Shajan, Madushanka Manathunga, Andreas Goetz, Kenneth Merz
Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open- source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and…
AKHIL SHAJAN, Madushanka Manathunga, Andreas Goetz, Kenneth Merz
Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open-source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and…
Ying Chen, Yue Tang, Bin Jiang, Yinan Zhao + 5 more
'Xianghong Tang' 'Xinyu Zhou' 'Wing Shing Chan' 'Liheng Zhou'] To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative…
GilHwan Kim, Haider A. Chishty, Fabrizio Sergi
Human-in-the-loop (HIL) optimization is a control paradigm used for tuning the control parameters of human-interacting devices while accounting for variability among individuals. A limitation of state-of-the-art HIL optimization algorithms such as Bayesian Optimization (BO) is that they assume that the relationship…
Florian Gisperg, Robert Klausser, Mohamed Elshazly, Julian Kopp + 2 more
'Eva Přáda Brichtová' 'Oliver Spadiut'] Title: ABSTRACT Bayesian optimization is a stochastic, global black-box optimization algorithm. By combining Machine Learning with decision-making, the algorithm can optimally utilize information gained during experimentation to plan further experiments-while balancing…
Hugo Silva, Martha White
Network? Authors: ['Hugo Silva' 'Martha White'] Oftentimes, machine learning applications using neural networks involve solving discrete optimization problems, such as in pruning, parameter-isolation-based continual learning and training of binary networks. Still, these discrete problems are combinatorial in nature and…
Søren Bertelsen, Sigurd Carlsen, Søren Furbo, Morten Bormann Nielsen + 2 more
an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use Authors: ['Søren Bertelsen' 'Sigurd Carlsen' 'Søren Furbo' 'Morten Bormann Nielsen' 'Aksel Obdrup' 'Rolf Taaning'] ProcessOptimizer is a Python package designed to provide…
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…
Authors not listed
Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…