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Computing the connected components of a graph is a fundamental problem in algorithmic graph theory. A major question in this area is whether we can compute connected components in $o(\log n)$ parallel time. Recent works showed an…

Data Structures and Algorithms · Computer Science 2025-01-31 Alireza Farhadi , S. Cliff Liu , Elaine Shi

Circuit routing is a fundamental problem in designing electronic systems such as integrated circuits (ICs) and printed circuit boards (PCBs) which form the hardware of electronics and computers. Like finding paths between pairs of…

Artificial Intelligence · Computer Science 2020-06-25 Youbiao He , Forrest Sheng Bao

We study the classic Euclidean Minimum Spanning Tree (MST) problem in the Massively Parallel Computation (MPC) model. Given a set $X \subset \mathbb{R}^d$ of $n$ points, the goal is to produce a spanning tree for $X$ with weight within a…

Data Structures and Algorithms · Computer Science 2023-08-02 Rajesh Jayaram , Vahab Mirrokni , Shyam Narayanan , Peilin Zhong

A class of neural networks that gained particular interest in the last years are neural ordinary differential equations (neural ODEs). We study input-output relations of neural ODEs using dynamical systems theory and prove several results…

Dynamical Systems · Mathematics 2023-09-29 Christian Kuehn , Sara-Viola Kuntz

Model Predictive Control (MPC) is an optimal control algorithm with strong stability and robustness guarantees. Despite its popularity in robotics and industrial applications, the main challenge in deploying MPC is its high computation…

Systems and Control · Electrical Eng. & Systems 2024-12-31 Camilo Gonzalez , Houshyar Asadi , Lars Kooijman , Chee Peng Lim

As the artificial intelligence community advances into the era of large models with billions of parameters, distributed training and inference have become essential. While various parallelism strategies-data, model, sequence, and…

Machine Learning · Computer Science 2025-03-13 Ruifeng She , Bowen Pang , Kai Li , Zehua Liu , Tao Zhong

We study two fundamental optimization problems: (1) scaling a symmetric positive definite matrix by a positive diagonal matrix so that the resulting matrix has row and column sums equal to 1; and (2) minimizing a quadratic function subject…

Data Structures and Algorithms · Computer Science 2025-04-30 Adrian Vladu

The rapid evolution of quantum devices fuels concerted efforts to experimentally establish quantum advantage over classical computing. Many demonstrations of quantum advantage, however, rely on computational assumptions and face…

Many real-world physics and engineering problems arise in geometrically complex domains discretized by meshes for numerical simulations. The nodes of these potentially irregular meshes naturally form point clouds whose limited tractability…

Machine Learning · Computer Science 2025-06-17 Shirin Hosseinmardi , Ramin Bostanabad

What makes a class of quantum circuits efficiently classically simulable on average? I present a framework that applies harmonic analysis of groups to circuits with a structure encoded by group parameters. Expanding the circuits in a…

Quantum Physics · Physics 2024-10-18 Cristina Cirstoiu

Persistence diagrams (PDs) are now routinely used to summarize the underlying topology of complex data. Despite several appealing properties, incorporating PDs in learning pipelines can be challenging because their natural geometry is not…

Machine Learning · Statistics 2018-11-14 Théo Lacombe , Marco Cuturi , Steve Oudot

Miller and Reif's FOCS'85 classic and fundamental tree contraction algorithm is a broadly applicable technique for the parallel solution of a large number of tree problems. Additionally it is also used as an algorithmic design technique for…

Data Structures and Algorithms · Computer Science 2021-11-04 MohammadTaghi Hajiaghayi , Marina Knittel , Hamed Saleh , Hsin-Hao Su

In this paper, we study parallel algorithms for the correlation clustering problem, where every pair of two different entities is labeled with similar or dissimilar. The goal is to partition the entities into clusters to minimize the number…

Data Structures and Algorithms · Computer Science 2023-07-14 Nairen Cao , Shang-En Huang , Hsin-Hao Su

We study the problem of finding connected components in the Adaptive Massively Parallel Computation (AMPC) model. We show that when we require the total space to be linear in the size of the input graph the problem can be solved in…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-04-17 Rustam Latypov , Jakub Łącki , Yannic Maus , Jara Uitto

Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc.. In this paper we present progress in…

Computer Vision and Pattern Recognition · Computer Science 2020-05-21 Rui Zhang , Conrad Albrecht , Wei Zhang , Xiaodong Cui , Ulrich Finkler , David Kung , Siyuan Lu

Along with the development of AI democratization, the machine learning approach, in particular neural networks, has been applied to wide-range applications. In different application scenarios, the neural network will be accelerated on the…

Quantum Physics · Physics 2020-12-21 Weiwen Jiang , Jinjun Xiong , Yiyu Shi

We give a general method of construting quantum circuit for random \QTR{it}{satisfiability} (SAT) problems with the basic logic gates such as multi-qubit controlled-NOT and NOT gates. The sizes of these circuits are almost the same as the…

Quantum Physics · Physics 2007-05-23 Xinhua Peng , Xiwen Zhu , Kelin Gao

The computation of the diameter is one of the most central problems in distributed computation. In the standard CONGEST model, in which two adjacent nodes can exchange $O(\log n)$ bits per round (here $n$ denotes the number of nodes of the…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-01-12 François Le Gall , Frédéric Magniez

Branch and bound algorithms have been developed for reliability analysis of coherent systems. They exhibit a set of advantages; in particular, they can find a computationally efficient representation of a system failure or survival event,…

Optimization and Control · Mathematics 2024-10-31 Ji-Eun Byun , Hyeuk Ryu , Daniel Straub

Massively parallel hardware (GPUs) and long sequence data have made parallel algorithms essential for machine learning at scale. Yet dynamical systems, like recurrent neural networks and Markov chain Monte Carlo, were thought to suffer from…

Numerical Analysis · Mathematics 2026-03-18 Xavier Gonzalez
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