25 papers · ranked by Valyu relevance
Sarthak Chatterjee, Subhro Das, Sérgio Pequito
Solving optimization problems is a recurrent theme across different fields, including large-scale machine learning systems and deep learning. Often in practical applications, we encounter objective functions where the Hessian is ill-conditioned, which precludes us from using optimization algorithms utilizing…
Jonas Kuckling, Thomas Stützle, Mauro Birattari, José Manuel Galán
Iterative improvement is an optimization technique that finds frequent application in heuristic optimization, but, to the best of our knowledge, has not yet been adopted in the automatic design of control software for robots. In this work, we investigate iterative improvement in the context of the automatic modular…
Raian Noufel Lefgoum
In this paper we study a new approach in optimization that aims to search a large domain D where a given function takes large, small or specific values via an iterative optimization algorithm based on the gradient. We show that the objective function used is not directly optimizable, however, we use a trick to…
Yang Yang, Marius Pesavento
—In this paper, we propose a successive pseudo-convex approximation algorithm to efficiently compute stationary points for a large class of possibly nonconvex optimization problems. The stationary points are obtained by solving a sequence of successively refined approximate problems, each of which is much easier to…
Kazunori D Yamada
In the deep learning era, a gradient descent method is the most common method to optimize parameters of neural networks. Among various mathematical optimization methods, a gradient descent method is the most naive method. Although controlling a learning rate of the method is necessary for quick convergence, the…
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…
Philippe A. Robert, Henrik Jönsson, Michael Meyer-Hermann
The modelling of biological systems often consists into differential equation models that need to be fitted to experimental data. During this complex process, the practical experience of the biologist and the theoretical abstraction of the modeller require back-and-forth refinements of the model, design of new…
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…
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…
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…
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…
Jesse A Sharp, Kevin Burrage, Matthew J Simpson
Optimal control theory provides insight into complex resource allocation decisions. The forward-backward sweep method (FBSM) is an iterative technique commonly implemented to solve two-point boundary value problems (TPBVPs) arising from the application of Pontryagin’s Maximum Principle (PMP) in optimal control. In this…
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…
Louise Rixon Fuchs, Atsuto Maki, Andreas Gällström, Andrzej Stateczny
'Andrzej Stateczny'] Imaging and mapping sonars such as forward-looking sonars (FLS) and side-scan sonars (SSS) are sensors frequently used onboard autonomous underwater vehicles. To acquire information from around the vehicle, it is desirable for these sonar systems to insonify a large area; thus, the sonar transmit…
Gonglin Yuan, Xiabin Duan, Wenjie Liu, Xiaoliang Wang + 3 more
'Zhou Sheng' 'Yongtang Shi'] Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains…
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…
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…
Fabian Fröhlich, Peter K. Sorger
Ordinary differential equation (ODE) models are widely used to describe biochemical processes, since they effectively represent mass action kinetics. Optimization-based calibration of ODE models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a…
Philipp‐Immanuel Schneider, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger
'Sven Burger'] Optical scatterometry is a method to measure the size and shape of periodic micro- or nanostructures on surfaces. For this purpose the geometry parameters of the structures are obtained by reproducing experimental measurement results through numerical simulations. We compare the performance of Bayesian…
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…