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Related papers: PID Tuning via Desired Step Response Curve Fitting

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For parabolic PDEs, we present a new certainty equivalence-based adaptive boundary control scheme with a least-squares identifier of an event-triggering type, where the triggering is based on the size of the regulation error (as opposed to…

Optimization and Control · Mathematics 2018-05-23 Iasson Karafyllis , Miroslav Krstic , Katerina Chrysafi

A new topology optimization method called the Proportional Topology Optimization (PTO) is presented. As a non-gradient method, PTO is simple to understand, easy to implement, and is also efficient and accurate at the same time. It is…

Computational Engineering, Finance, and Science · Computer Science 2016-05-30 Emre Biyikli , Albert C. To

Property Directed Reachability (PDR) is a powerful algorithm for formal verification of hardware and software systems, but its performance is highly sensitive to parameter configurations. Manual parameter tuning is time-consuming and…

Hardware Architecture · Computer Science 2026-04-01 Guangyu Hu , Chen Chen , Xiaofeng Zhou , Jiaxi Zhang , Wei Zhang , Hongce Zhang

We investigate the stochastic gradient descent (SGD) method where the step size lies within a banded region instead of being given by a fixed formula. The optimal convergence rate under mild conditions and large initial step size is proved.…

Optimization and Control · Mathematics 2023-04-10 Xiaoyu Wang , Ya-xiang Yuan

Parameter-efficient finetuning (PEFT) methods effectively adapt large language models (LLMs) to diverse downstream tasks, reducing storage and GPU memory demands. Despite these advantages, several applications pose new challenges to PEFT…

Machine Learning · Computer Science 2024-11-05 Baohao Liao , Christof Monz

Synthesis planning seeks an efficient sequence of chemical reactions that produce a target molecule. Typically, a pretrained single-step (autoregressive) retrosynthesis model is repeatedly invoked to generate such a sequence. Classifier…

Machine Learning · Computer Science 2026-05-14 Najwa Laabid , Vikas Garg

Many relevant problems in the area of systems and control, such as controller synthesis, observer design and model reduction, can be viewed as optimization problems involving dynamical systems: for instance, maximizing performance in the…

Optimization and Control · Mathematics 2023-11-15 Pascal Den Boef , Jos Maubach , Wil Schilders , Nathan van de Wouw

An adaptive algorithm for spectral proper orthogonal decomposition (SPOD) of mixed broadband-tonal turbulent flows is developed. Sharp peak resolution at tonal frequencies is achieved by locally minimizing the bias of the spectrum. Smooth…

Fluid Dynamics · Physics 2024-06-25 Brandon C. Y. Yeung , Oliver T. Schmidt

Stochastic Gradient Descent (SGD) is very useful in optimization problems with high-dimensional non-convex target functions, and hence constitutes an important component of several Machine Learning and Data Analytics methods. Recently there…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-11-11 Karl Bäckström , Marina Papatriantafilou , Philippas Tsigas

In this paper, we revisit \textsf{ROOT-SGD}, an innovative method for stochastic optimization to bridge the gap between stochastic optimization and statistical efficiency. The proposed method enhances the performance and reliability of…

Machine Learning · Statistics 2024-08-26 Chris Junchi Li

This work introduces a new method to efficiently solve optimization problems constrained by partial differential equations (PDEs) with uncertain coefficients. The method leverages two sources of inexactness that trade accuracy for speed:…

Optimization and Control · Mathematics 2019-05-20 Matthew J. Zahr , Kevin T. Carlberg , Drew P. Kouri

Large Language Models employing extended chain-of-thought (CoT) reasoning often suffer from the overthinking phenomenon, generating excessive and redundant reasoning steps that increase computational costs while potentially degrading…

Computation and Language · Computer Science 2026-04-14 Aryasomayajula Ram Bharadwaj

Learning rate schedules used in practice bear little resemblance to those recommended by theory. We close much of this theory/practice gap, and as a consequence are able to derive new problem-adaptive learning rate schedules. Our main…

Machine Learning · Computer Science 2024-10-31 Aaron Defazio , Ashok Cutkosky , Harsh Mehta , Konstantin Mishchenko

In a standard optimization approach, the underlying process model is first identified at a given set of operating conditions and this updated model is, then, used to calculate the optimal conditions for the process. This two-step procedure…

Optimization and Control · Mathematics 2015-08-27 Jasdeep S. Mandur , Hector M. Budman

Selecting an effective step-size is a fundamental challenge in first-order optimization, especially for problems with non-Euclidean geometries. This paper presents a novel adaptive step-size strategy for optimization algorithms that rely on…

Optimization and Control · Mathematics 2025-10-14 Abbas Khademi , Antonio Silveti-Falls

This paper focuses on the design of sequential quadratic optimization (commonly known as SQP) methods for solving large-scale nonlinear optimization problems. The most computationally demanding aspect of such an approach is the computation…

Optimization and Control · Mathematics 2020-02-27 James V. Burke , Frank E. Curtis , Hao Wang , Jiashan Wang

This study presents an analytical method for tuning PI controllers in First-Order with Time Delay (FOTD) systems, leveraging the Lambert W function. The Lambert W function enables exact pole placement, yielding analytical expressions for PI…

Systems and Control · Electrical Eng. & Systems 2026-01-22 Senol Gulgonul

In this work, we introduce a control framework that combines model-based footstep planning with Reinforcement Learning (RL), leveraging desired footstep patterns derived from the Linear Inverted Pendulum (LIP) dynamics. Utilizing the LIP…

Robotics · Computer Science 2024-08-06 Ho Jae Lee , Seungwoo Hong , Sangbae Kim

Pruning is an effective method to reduce the memory footprint and computational cost associated with large natural language processing models. However, current pruning algorithms either only focus on one pruning category, e.g., structured…

Computation and Language · Computer Science 2022-05-24 Zhewei Yao , Xiaoxia Wu , Linjian Ma , Sheng Shen , Kurt Keutzer , Michael W. Mahoney , Yuxiong He

A Fractional adaptive PID (FPID) controller for a robot manipulator will be proposed. The PID parameters have been optimized by Genetic algorithm. The proposed controller is found robust by means of simulation in a tracking job. The…

Adaptation and Self-Organizing Systems · Physics 2012-06-12 H. Delavari , R. Ghaderi , N. A. Ranjbar , S. H. HosseinNia , S. Momani