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14th International Conference on algorithms and Complexity, CIAC 2025

作者： Coste, Louann Li, Ruoying Storandt, Sabine Töpfer, Tobias École Normale Supérieure de Lyon Lyon France University of Konstanz Konstanz Germany

ISBN: (纸本)9783031929342

To speed up the computation of shortest path distances between pairs of query nodes in a weighted graph, it is common to precompute a distance oracle. One of the most successful of these oracles is Hub Labeling (HL). Its good practical performance spurred research on the complexity of different HL variants, their structural properties, and suitable construction algorithms. Landmark Hub Labeling (LHL) is a generalization of HL. It enables the construction of distance oracles with similar query time but smaller space consumption than HL, which is an important aspect for practical usability. However, the complexity of LHL has not been thoroughly investigated so far. In this paper, we provide a complete picture of the complexity landscape of different LHL variants. Surprisingly, the only known HL variant for which a minimum-size oracle can be computed in polynomial time (namely ranked hierarchical HL) turns out to be NP-hard when generalized to LHL. We complement our hardness results with suitable approximation algorithms for all studied LHL variants based on novel structural insights. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： approximation algorithms

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The Bayes Clinical Statistical Accuracy Optimization Using Expectation-Maximization Iteration

The Bayes Clinical Statistical Accuracy Optimization Using E...

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IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)

作者： Yu-Hung Yeh Ying-Yun Chen Jiun-Lin Yan Ta-Sung Lee Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan R.O.C. Department of Neurosurgery Keelung Chang Gung Memorial Hospital Keelung Taiwan R.O.C.

ISBN: (数字)9798350383409

ISBN: (纸本)9798350383416

Bayes classifier employs a statistical model to categorize data. It is regarded as the most effective classifier when its probabilistic models are able to extract sufficient information from reality. Nevertheless, it is unfeasible to obtain an adequate quantity of data to construct a precise statistical model in certain significant instances. The proposed algorithm aims to enhance the precision of the Bayes statistical model through the incorporation of the Expectation-Maximization (EM) algorithm. The simulation results indicate that the accuracy of the Bayes probabilistic model can be enhanced by approximately 10% under specific conditions. Furthermore, our findings revealed that each medical feature contributes a unique increment to the statistical model.

关键词： Accuracy Simulation Biological system modeling Probabilistic logic approximation algorithms Data processing Data models Bayes methods Classification algorithms Medical diagnostic imaging

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FPGA Implementation of Range Migration Algorithm for Synthetic Aperture Radar Using Simulink HDL Coder

FPGA Implementation of Range Migration Algorithm for Synthet...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Yang Meng Haoyi Yang Bocheng Chen Guoping Chen Anyong Qing School of Optoelectronic Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Electrical Engineering Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

The millimeter-wave synthetic aperture radar echo data processing is a vital step in target reconstruction, and this phase is inherently time-consuming, demanding significant computational resources. To expedite the reconstruction process, Field-Programmable Gate Arrays (FPGA) can be employed. Nevertheless, utilizing FPGA for implementation often entails grappling with complex hardware description languages such as Verilog or VHDL, which are difficult and time-consuming to develop. To overcome these challenges, a hardware architecture based on the range migration algorithm (RMA) is proposed in this paper. We utilize the Simulink HDL Coder toolbox to encapsulate the algorithm into an IP Core, subsequently verifying it on the Xilinx XC7Z020. The test results show that operating at a clock frequency of 100MHz, target reconstruction with original data of size 256×151×64 requires approximately 26.74 ms. This represents a notable acceleration, being about 7.2 times faster than CPU processing in Matlab.

关键词： VHDL Software packages Computer architecture Millimeter wave radar approximation algorithms Radar antennas Hardware

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Temporal Elections: Welfare, Strategyproofness, and Proportionality

arXiv

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arXiv 2024年

作者： Elkind, Edith Neoh, Tzeh Yuan Teh, Nicholas Northwestern University United States Agency for Science Technology and Research Singapore University of Oxford United Kingdom

We investigate a model of sequential decision-making where a single alternative is chosen at each round. We focus on two objectives—utilitarian welfare (UTIL) and egalitarian welfare (EGAL)—and consider the computational complexity of maximizing these objectives, as well as their compatibility with strategyproofness and proportionality. We observe that maximizing UTIL is easy, but the corresponding decision problem for EGAL is NP-complete even in restricted cases. We complement this hardness result for EGAL with parameterized complexity analysis and an approximation algorithm. Additionally, we show that, while a mechanism that outputs an outcome that maximizes UTIL is strategyproof, all deterministic mechanisms for computing outcomes that maximize EGAL fail a very weak variant of strategyproofness, called non-obvious manipulability (NOM). However, we show that when agents have non-empty approval sets at each timestep, choosing an EGAL-maximizing outcome while breaking ties lexicographically satisfies NOM. Regarding proportionality, we prove that a proportional (PROP) outcome can be computed efficiently, but finding an outcome that maximizes UTIL while guaranteeing PROP is NP-hard. We also derive upper and lower bounds on the (strong) price of proportionality with respect to UTIL and EGAL. Some of our results extend to p-mean welfare measures other than EGAL and UTIL. Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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Evasion Attacks on Tree-Based Machine Learning Models

Evasion Attacks on Tree-Based Machine Learning Models

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Carson Koball Yong Wang The Beacom College of Computer and Cyber Sciences Dakota State University Madison SD USA

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Various machine learning algorithms exist, each maintaining different assumptions about the underlying data or the function they aim to approximate. One such class of algorithms include tree-based models, which develop decision graphs that determine model behavior. In this research, we focus on developing two attacks that target tree-based models: one attack targets single tree-based models and the other targets ensemble tree-based models. We evaluate the two proposed attack algorithms on the popular MNIST handwritten digits dataset. Evading samples generated from the testing session are analyzed through the generated perturbation from each sample. We find that the evasion attacks are largely successful in the main objectives of evading: forcing incorrect model output and maintaining the stealthiness of the attack. Additionally, we find that the proposed attack algorithms perform well in the more stricter attack setting of targeted evasion.

关键词： Adaptation models Visualization Electric potential Machine learning algorithms Perturbation methods approximation algorithms Decision trees Random forests Testing Periodic structures

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Improved Sliding Mode Control for Airborne Electro-Optical Stabilized Platforms Based on Disturbance Observer

Improved Sliding Mode Control for Airborne Electro-Optical S...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Mengchen Yan Xiangwei Mou Rijun Wang Juan Hu College of Electronic and Information Engineering/Integrated Circuits Guangxi Normal University GXNU Guilin China Teachers College for Vocational and Technical Education Guangxi Normal University GXNU Guilin China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

To address low control accuracy, long adjustment times, and disturbances such as external interference and internal parameter variations, this paper proposes an Improved Non-Singular Fast Terminal Sliding Mode Control (I-NFTSMC) algorithm based on a disturbance observer. The algorithm estimates and compensates for unknown disturbances in real-time and introduces a time-varying switching gain to reduce chattering from high-frequency switching. Simulation results show that the I-NFTSMC algorithm improves response speed by approximately 42% and reduces rise time by about 44% under disturbance conditions compared to traditional methods. This approach achieves overshoot-free and accurate tracking of input commands, significantly enhancing the stability and disturbance rejection capabilities of airborne electro-optical platforms.

关键词： Accuracy Simulation Switches Electro-optic effects approximation algorithms Disturbance observers Stability analysis Robustness Real-time systems Sliding mode control

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Cooperative Control of Vehicle Platoons with Unknown Dynamics using Neural Network-Enhanced Reinforcement Learning

Cooperative Control of Vehicle Platoons with Unknown Dynamic...

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International Conference on Mechatronics (ICM)

作者： Elham Yazdani Bejarbaneh Haiping Du School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong NSW Australia

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This study proposes an optimal cooperative control framework for heterogeneous vehicle platoons using a neural network (NN)-based reinforcement learning (RL) algorithm in an identifier-critic-actor framework. Typically, determining the optimal control policy requires solving the Hamilton-Jacobi-Bellman (HJB) equation, but the presence of nonlinear terms within the HJB equation makes deriving analytical solutions challenging. While RL algorithms can overcome this issue, existing NN-based RL approaches are inherently complex as their update rules are derived from gradient descent optimization of the squared approximation of the HJB equation. This complexity makes their implementation challenging for nonlinear multi-vehicle systems with unknown dynamics. The proposed control strategy for platooning leverages an NN-based RL algorithm, where the update rules are directly derived from the negative gradient of a positive function that is mathematically equivalent to the HJB equation. Additionally, an NN-based identifier is incorporated into the platooning control design to dynamically estimate unknown vehicle dynamics in real time. The methodology is experimentally validated using the high-fidelity Mixed Traffic Simulator (MiTaS) co-simulation platform, which combines the Simulation of Urban Mobility (SUMO) microscopic traffic simulator with MATLAB environment. Simulations results demonstrate the efficacy of the learned strategy in achieving optimal leader-tracking performance.

关键词： Heuristic algorithms Simulation Optimal control Artificial neural networks Reinforcement learning approximation algorithms Mathematical models Topology Vehicle dynamics Optimization

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algorithms and Hardness for Estimating Statistical Similarity

arXiv

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arXiv 2025年

作者： Bhattacharyya, Arnab Gayen, Sutanu Meel, Kuldeep S. Myrisiotis, Dimitrios Pavan, A. Vinodchandran, N.V. University of Warwick United Kingdom IIT Kanpur India University of Toronto Georgia Institute of Technology United States CNRS@CREATE LTD. France Iowa State University United States University of Nebraska-Lincoln United States

We study the problem of computing statistical similarity between probability distributions. For distributions P and Q over a finite sample space, their statistical similarity is defined as Sstat(P, Q):= ∑x min(P (x), Q(x)). Statistical similarity is a basic measure of similarity between distributions, with several natural interpretations, and captures the Bayes error in prediction and hypothesis testing problems. Recent work has established that, somewhat surprisingly, even for the simple class of product distributions, exactly computing statistical similarity is #P-hard. This motivates the question of designing approximation algorithms for statistical similarity. Our primary contribution is a Fully Polynomial-Time deterministic approximation Scheme (FPTAS) for estimating statistical similarity between two product distributions. To obtain this result, we introduce a new variant of the Knapsack problem, which we call the Masked Knapsack problem, and design an FPTAS to estimate the number of solutions of a multidimensional version of this problem. This new technical contribution could be of independent interest. Furthermore, we also establish a complementary hardness result. We show that it is NP-hard to estimate statistical similarity when P and Q are Bayes net distributions of in-degree 2. © 2025, CC BY.

关键词： approximation algorithms

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A Joint Gauss-Seidel and Neumann Series Precoding Scheme for a Downlink Massive MIMO System

A Joint Gauss-Seidel and Neumann Series Precoding Scheme for...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Nurulhuda Ismail Khairun Nidzam Ramli Ariffuddin Joret Mohamad Hairol Jabbar Norshidah Katiran Eddy Irwan Shah Saadon Dept. of Electronic Engineering Faculty of Electrical and Electronic Engineering University Tun Hussein Onn Malaysia Malaysia Dept. of Electrical Engineering Center of Diploma Studies University Tun Hussein Onn Malaysia Malaysia

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

In the downlink (DL) of a Time Division Duplex (TDD) massive MIMO system, the effect of multi-user interference is ameliorated efficiently by Zero Forcing (ZF) linear precoding, which may achieve an almost optimal sum-rate performance. However, ZF suffers from a high implementation complexity of $O(K^{3})$ owing to the inversion of a $K\times K$ channel correlation matrix due to its direct inversion method. To lessen this complexity, approximate matrix inversion by the Gauss-Seidel (GS) or Neumann series (NS) methods has been explored extensively. In this paper, the authors propose a joint Gauss-Seidel Neumann series (JGSNS) algorithm that approximates square matrix inversion with faster convergence speed and fewer FLOPs than the Neumann series. The performance of the precoding scheme is evaluated in a DL, TDD massive MIMO system as a function of antenna array size $M$ and number of simultaneous users K.

关键词： Precoding Bit error rate Memory management Massive MIMO Rayleigh channels Downlink approximation algorithms Complexity theory Iterative methods Antenna arrays

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Stochastic Vector Approximate Message Passing with Applications to Phase Retrieval

Stochastic Vector Approximate Message Passing with Applicati...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hajime Ueda Shun Katakami Masato Okada Graduate School of Frontier Sciences The University of Tokyo Kashiwa Chiba Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Phase retrieval refers to the problem of recovering a high-dimensional vector ${\mathbf{x}} \in {\mathbb{C}^N}$ from the magnitude of its linear transform z = Ax, observed through a noisy channel. To improve the ill-posed nature of the inverse problem, it is a common practice to observe the magnitude of linear measurements z (1) = A (1) x,…, z (L) = A (L) x using multiple sensing matrices A (1) ,…, A (L) , with ptychographic imaging being a remarkable example of such strategies. Inspired by existing algorithms for ptychographic reconstruction, we introduce stochasticity to Vector Approximate Message Passing (VAMP), a computationally efficient algorithm applicable to a wide range of Bayesian inverse problems. By testing our approach in the setup of phase retrieval, we show the superior convergence speed of the proposed algorithm.

关键词： Inverse problems Message passing Signal processing algorithms Imaging approximation algorithms Vectors Bayes methods Probes Image reconstruction Convergence

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