Greedy Algorithm

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CLIPPER Beyond Shortlisting: Auditable Decision Support for Changing Municipal Micromobility Policies
Sep 30, 2026Julian Teusch, Jörg Philipp Müller, Monika SesterUrban Mobility Long-Run Coverage Guarantees
Institute of Computer Science Clausthal University of Technology Clausthal-Zellerfeld, Germany · Institute of Cartography and Geoinformatics Leibniz University Hannover Hannover, Germany
Approximate Speculative Decoding
Aug 4, 2026Yuannuo Feng, Zegang Peng, Yuxin Xie +5Speculative Decoding Truncation
School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China · Department of Precision Instrument, Tsinghua University, Beijing, China · Faculty of Engineering, The University of Hong Kong, Hong Kong SAR, China +2
Think in Sets for Streaming Video Token Compression
Aug 2, 2026Moxu Duan, Jingwen Fu, Yuwang WangToken Compression Task-Aware Compression
Beijing Jiaotong University · Zhongguancun Academy · Tsinghua University
The Greedy Advantage in Finite-Horizon Bandits
Jul 31, 2026Kai Zhou, Michael Lingzhi Li, Kai WangStochastic Multi-Armed Bandits Interval Regret
Tsinghua University · Harvard Business School
Why does Greedy Search produce Optimal Clustering Outcomes? A Fixed-Core Assignment Theory
Jul 27, 2026Kaifeng Zhang, Kai Ming Ting, Sanjay ChawlaClustering Greedy Algorithm
School of Artificial Intelligence Nanjing University, China · Qatar Computing Research Institute, HBKU
Greedy dynamical meta-learning
Jul 27, 2026Aria YomMeta-Learning Gradient-Based Optimization
Rhodora
Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning
Jul 15, 2026Shiqi Zhang, Marius Faiß, Ariana Strandburg-Peshkin +1Passive Acoustic Monitoring Active Learning
1Audio Research Group, Tampere University, Tampere, Finland · Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior · 3International Max Planck Research School for Quantitative Behavior, Ecology and Evolution +1
The Approximation Ratio for the Risk of Myopic Bayesian Active Learning for Linear Regression
Jul 7, 2026Stephen MussmannPool-Based Active Learning Active Learning
School of Computer Science Georgia Institute of Technology
The Benchmark Illusion: Pruned LLMs Can Pass Multiple Choice but Fail to Answer
Jun 16, 2026Rui Wen, Lu Sun, Jiayang Liu +3Multiple-Choice Questions Greedy Algorithm
Institute of Science Tokyo · Tohoku University · Nanyang Technological University +2
Importance-Aware Scheduling for High-Dimensional Hyperparameter Optimization
Jun 8, 2026Ruinan Wang, Ian Nabney, Mohammad GolbabaeeGeneral Grid Search Framework High-Dimensional
University of Bristol, Bristol, United Kingdom
Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
May 15, 2026Heqiang Wang, Weihong Yang, Zheyuan Yang +4Multimodal Industrial Anomaly Detection Schedulers
Pengcheng Laboratory, Shenzhen, 518066, China · Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
Greedy or not, here I come: Language production under vocabulary constraints in humans and resource-rational models
May 14, 2026Thomas Hikaru Clark, Sihan Chen, Laura NicolaeLinguistics Greedy Algorithm
Department of Brain and Cognitive Sciences, MIT · Department of Economics, Harvard University
Curvature Beyond Positivity: Greedy Guarantees for Arbitrary Submodular Functions
May 8, 2026Yixin Chen, Alan KuhnleSubmodular Functions Local Curvature
Department of Computer Science & Engineering, Texas A&M University
Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare
May 3, 2026Maheed H. Ahmed, Mahsa GhasemiAlgorithmic Fairness Stochastic Multi-Armed Bandits
Electrical and Computer Engineering Purdue University
Precision autotuning for linear solvers via contextual bandit-based RL
Jan 2, 2026Erin Carson, Xinye ChenSolver Iterations Full-Precision Performance
Charles University Prague, Czech · Sorbonne Université, CNRS, LIP6 Paris, France
Faster and Simpler Greedy Algorithm for $k$ -Median and $k$ -Means
Jul 15, 2024Max Dupré la Tour, David SaulpicK-Means Approximation Algorithms

Sep 30, 2026, cs.ROJ/K move · Enter open · S save

CLIPPER Beyond Shortlisting: Auditable Decision Support for Changing Municipal Micromobility Policies

Julian Teusch, Jörg Philipp Müller, Monika Sester

Institute of Computer Science Clausthal University of Technology Clausthal-Zellerfeld, Germany · Institute of Cartography and Geoinformatics Leibniz University Hannover Hannover, Germany

Urban Mobility Long-Run Coverage Guarantees Shortest Paths Greedy Algorithm Software Engineering Decision Support

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In municipal planning workshops, planners and other stakeholders compare shared-micromobility parking policies by varying no-parking zones, retained sites, spacing, or area allocations. Each edit changes feasible sites and how much demand they cover, so the alternative must be reoptimized on the same spatial data. Full-set greedy, the transparent reference for this task, takes tens of seconds per alternative at city scale. We present Constraint-exact Low-latency Iterative Planning with Pooled Evaluation and Replay (CLIPPER), an optimizer with audit functions developed for requirements elicited with the City of Braunschweig. In each greedy round, it forms a deterministic candidate pool of bounded size, computes how much still-uncovered demand each candidate would add, and rejects candidates that violate an active constraint. An optional offline audit scans every remaining feasible candidate and records what the restricted pool omitted. We evaluate these functions on complete eleven-state edit chains (

E_0,\ldots,E_{10}

) in Braunschweig, Munich, and Berlin. With

K=1024

candidates per group, the fixed-width mode CLIPPER-F has mean coverage gaps to full-set greedy under the same policy of 0.245, 0.003, and 0.001 percentage points in Braunschweig, Munich, and Berlin, respectively, while mean rollout time falls by factors of 13.6--28.9; no audited run terminates while a candidate outside the pool could still increase coverage. Plans computed from two checksummed versions of Braunschweig's official no-parking-zone data differ in 30 of about 540 selected sites although coverage moves by only about 0.1 percentage points. These changes still require municipal assessment and implementation. The findings inform a proposed municipal process that versions policy inputs, reports site changes beside coverage, and scans the full candidate set before a final decision.

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