“‘Algorithms’ Tag”,2019-09-07 (math)⁠:

Bibliography for tag cs/algorithm, most recent first: 3 related tags, 231 annotations, & 139 links (parent).

• See Also
• Gwern
  • “The Scaling Hypothesis”, Gwern2020
  • “Computer Optimization: Your Computer Is Faster Than You Think”, Gwern2021
• Links
  • “Float Self-Tagging”, Melançon et al 2024
  • “Breaking Bad: How Compilers Break Constant-Time”, Schneider et al 2024
  • “It’s Not Easy Being Green: On the Energy Efficiency of Programming Languages”, Kempen et al 2024
  • “Glue and Coprocessor Architectures”, Buterin2024
  • “Amit’s A^✱ Pages”, Patel2024
  • “Writing Commit Messages”, Tatham2024
  • “Polyamorous Scheduling”, Gąsieniec et al 2024
  • “Beyond A^✱: Better Planning With Transformers via Search Dynamics Bootstrapping (Searchformer)”, Lehnert et al 2024
  • “Hamiltonicity of Expanders: Optimal Bounds and Applications”, Draganić et al 2024
  • “Efficient Parallelization of an Ubiquitous Sequential Computation”, Heinsen2023
  • “Towards Automatic Design of Factorio Blueprints”, Patterson et al 2023
  • “U-Net CNN in APL: Exploring Zero-Framework, Zero-Library Machine Learning”, Hsu & Serrão2023
  • “Blockwise Parallel Transformer for Long Context Large Models”, Liu & Abbeel2023
  • “You And Your Research”, Hamming2023
  • “Distinct Elements in Streams: An Algorithm for the (Text) Book”, Chakraborty et al 2023
  • “Monolith: Real Time Recommendation System With Collisionless Embedding Table”, Liu et al 2022
  • “A Library for Representing Python Programs As Graphs for Machine Learning”, Bieber et al 2022
  • “TextWorldExpress: Simulating Text Games at One Million Steps Per Second”, Jansen & Côté2022
  • “Learning With Combinatorial Optimization Layers: a Probabilistic Approach”, Dalle et al 2022
  • “Overwatch: Learning Patterns in Code Edit Sequences”, Zhang et al 2022
  • “Heisenbugs: The Most Elusive Kind of Bug, and How to Capture Them With Perfect Replayability—Eliminate Heisenbugs and Endless Debugging Sessions!”, Ovadia2022
  • “Progress in Mathematical Programming Solvers 2001^–₁₉2020”, Koch et al 2022
  • “Searching for Cyclic TV Reference Paradoxes”, Pinheiro2022
  • “Fast Text Placement Scheme for ASCII Art Synthesis”, Chung & Kwon2022
  • “Monarch: Expressive Structured Matrices for Efficient and Accurate Training”, Dao et al 2022
  • “Maximum Flow and Minimum-Cost Flow in Almost-Linear Time”, Chen et al 2022
  • “Clock: 解説”
  • “Bamboo Trimming Revisited: Simple Algorithms Can Do Well Too”, Kuszmaul2022
  • “What Goes into Making an OS to Be Unix Compliant Certified?”, Lambert2022
  • “Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow”, Tambon et al 2021
  • “Improving Real-Time Rendering of Dynamic Digital Characters in Cycles”, Dietrich2021
  • “Real Time Cluster Path Tracing”, Xie et al 2021
  • “Small-Amp: Test Amplification in a Dynamically Typed Language”, Abdi et al 2021
  • “Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs”, Sonnerat et al 2021
  • “Hacker News Folk Wisdom on Visual Programming”, Keer2021
  • “Real-Time Neural Radiance Caching for Path Tracing”, Müller et al 2021
  • “Randomness In Neural Network Training: Characterizing The Impact of Tooling”, Zhuang et al 2021
  • “How Developers Choose Names”, Feitelson et al 2021
  • “Pretrained Transformers As Universal Computation Engines”, Lu et al 2021
  • “Entropy Trade-Offs in Artistic Design: A Case Study of Tamil kolam”, Tran et al 2021
  • “Investment vs. Reward in a Competitive Knapsack Problem”, Neumann & Gros2021
  • “MLGO: a Machine Learning Guided Compiler Optimizations Framework”, Trofin et al 2021
  • “NNUE: The Neural Network of the Stockfish Chess Engine”, Goucher2021
  • “I Know What You Bought At Chipotle for $9.81 by Solving A Linear Inverse Problem”, Fleder & Shah2020
  • “Presyn: Modeling Black-Box Components With Probabilistic Synthesis”, Collie et al 2020
  • “Why Johnny Won’t Upgrade”, Mattheij2020
  • “Optimal Peanut Butter and Banana Sandwiches”, Rosenthal2020
  • “A Time Leap Challenge for SAT Solving”, Fichte et al 2020
  • “Measuring Hardware Overhang”, hippke 2020
  • “A Bayesian Approach to the Simulation Argument”, Kipping2020
  • “Algorithms With Predictions”, Mitzenmacher & Vassilvitskii2020
  • “BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits”, Tiwari et al 2020
  • “Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing”, Dai et al 2020
  • “Lessons Learned from Bugs in Models of Human History”, Ragsdale et al 2020
  • “Measuring the Algorithmic Efficiency of Neural Networks”, Hernandez & Brown2020
  • “Tech Notes: The Success and Failure of Ninja”
  • “Bringing GNU Emacs to Native Code”, Corallo et al 2020
  • “Learning-Based Memory Allocation for C++ Server Workloads”, Maas et al 2020
  • “Reinforcement Learning for Combinatorial Optimization: A Survey”, Mazyavkina et al 2020
  • “The History of the URL”, Bloom2020
  • “Quantifying Independently Reproducible Machine Learning”, Raff2020
  • “Solving Billion-Scale Knapsack Problems”, Zhang et al 2020
  • “Taxonomy of Real Faults in Deep Learning Systems”, Humbatova et al 2019
  • “They Might Never Tell You It’s Broken”, Chevalier-Boisvert2019
  • “Co-Dfns: A Data Parallel Compiler Hosted on the GPU”, Hsu2019c
  • “Hyrum’s Law: An Observation on Software Engineering”, Wright2019
  • “Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning”, Vinyals et al 2019
  • “Local-First Software: You Own Your Data, in spite of the Cloud [Paper]”, Kleppmann et al 2019
  • “Gated Linear Networks”, Veness et al 2019
  • “A Step Toward Quantifying Independently Reproducible Machine Learning Research”, Raff2019
  • “Different Languages, Similar Encoding Efficiency: Comparable Information Rates across the Human Communicative Niche”, Coupé et al 2019
  • “A View on Deep Reinforcement Learning in System Optimization”, Haj-Ali et al 2019
  • “Moral Permissibility of Action Plans”, Lindner et al 2019
  • “ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data”, Stehle & Jacobsen2019
  • “Real-World Dynamic Programming: Seam Carving”, Das2019
  • “Local-First Software: You Own Your Data, in spite of the Cloud [Web]”, Kleppmann et al 2019
  • “GAP: Generalizable Approximate Graph Partitioning Framework”, Nazi et al 2019
  • “Parsing Gigabytes of JSON per Second”, Langdale & Lemire2019
  • “AutoPhase: Compiler Phase-Ordering for High Level Synthesis With Deep Reinforcement Learning”, Haj-Ali et al 2019
  • “Reinventing the Wheel: Discovering the Optimal Rolling Shape With PyTorch”, Wiener2019
  • “Meta-Learning Neural Bloom Filters”, Rae2019
  • “SageDB: A Learned Database System”, Kraska2019
  • “Test Driving ‘Power of Two Random Choices’ Load Balancing”, Tarreau2019
  • “Slow Software”, McGranaghan2018
  • “Learning to Perform Local Rewriting for Combinatorial Optimization”, Chen & Tian2018
  • “How to Shuffle a Big Dataset”, Hardin2018
  • “Deterministic Implementations for Reproducibility in Deep Reinforcement Learning”, Nagarajan et al 2018
  • “Learning to Optimize Join Queries With Deep Reinforcement Learning”, Krishnan et al 2018
  • “Always Measure One Level Deeper: Performance Measurements Often Go Wrong, Reporting Surface-Level Results That Are More Marketing Than Science”, Ousterhout2018
  • “Learning to Optimize Tensor Programs”, Chen et al 2018
  • “Optimizing Query Evaluations Using Reinforcement Learning for Web Search”, Rosset et al 2018
  • “Learning Memory Access Patterns”, Hashemi et al 2018
  • “Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions”, Vasilache et al 2018
  • “Innovation and Cumulative Culture through Tweaks and Leaps in Online Programming Contests”, Miu et al 2018
  • “The Case for Learned Index Structures”, Kraska et al 2017
  • “Automatic Differentiation in PyTorch”, Paszke et al 2017
  • “From Punched Cards to Flat Screens: A Technical Autobiography”, Hazel2017
  • “DAG Reduction: Fast Answering Reachability Queries”, Zhou et al 2017
  • “Stochastic Constraint Programming As Reinforcement Learning”, Prestwich et al 2017
  • “Learning to Superoptimize Programs”, Bunel et al 2017
  • “Web Bloat”, Luu2017
  • “Resource-Efficient Machine Learning in 2 KB RAM for the Internet of Things”, Kumar et al 2017
  • “Machine Learning for Systems and Systems for Machine Learning”, Dean2017
  • “P≟NP § AI”, Aaronson2017 (page 5)
  • “Neural Combinatorial Optimization With Reinforcement Learning”, Bello et al 2016
  • “The Doodle Theorem, and Beyond: Colin Wright Juggles Euler, Doodling and Millennium Problems”, Wright2016
  • “Coz: Finding Parallel Code That Counts With Causal Profiling”, Curtsinger & Berger2016
  • “A Discrete and Bounded Envy-Free Cake Cutting Protocol for Any Number of Agents”, Aziz & Mackenzie2016
  • “Why WhatsApp Only Needs 50 Engineers for Its 900M Users: One of the (many) Intriguing Parts of the WhatsApp Story Is That It Has Achieved Such Enormous Scale With Such a Tiny Team”, Metz2015
  • “Scalability! But at What COST?”, McSherry et al 2015
  • “Inferring Algorithmic Patterns With Stack-Augmented Recurrent Nets”, Holdings et al 2015
  • “The Ph.D. Grind: A Ph.D. Student Memoir”, Guo2015
  • “The Misfortunes of a Trio of Mathematicians Using Computer Algebra Systems—Can We Trust in Them?”, Durán et al 2014
  • “Always Bet on Text”, graydon2 2014
  • “The Mystery Machine: End-To-End Performance Analysis of Large-Scale Internet Services”, Chow et al 2014 (page 2)
  • “Core-Guided MaxSAT With Soft Cardinality Constraints”, Morgado et al 2014
  • “Algorithmic Progress in Six Domains”, Grace2013
  • “Intelligence Explosion Microeconomics”, Yudkowsky2013
  • “Homotopy Groups of Suspended Classifying Spaces: An Experimental Approach”, Romero & Rubio2013
  • “Bounded Kolmogorov Complexity Based on Cognitive Models”, Strannegård et al 2013
  • “Really Automatic Scalable Object-Oriented Reengineering”, Trudel et al 2013
  • “Planning As Satisfiability: Heuristics”, Rintanen2012
  • “The Algebraic Combinatorial Approach for Low-Rank Matrix Completion”, Király et al 2012
  • “Evaluating the Design of the R Language: Objects and Functions for Data Analysis”, Morandat et al 2012
  • “The International SAT Solver Competitions”, Järvisalo et al 2012
  • “Uniform Random Generation of Large Acyclic Digraphs”, Kuipers & Moffa2012
  • “National Cryptologic Museum Opens New Exhibit on Dr. John Nash”, NSA 2012
  • “How Trello Is Different”, Spolsky2012
  • “A Brief History of NP-Completeness, 1954^–₅₈2012_12ya”, Johnson2012
  • “Cutting the Pipe: Achieving Sub-Second Iteration Times”, 5.1.1 2012
  • “STEPS Toward Expressive Programming Systems: “A Science Experiment””, Ohshima et al 2012 (page 2)
  • “Why Philosophers Should Care About Computational Complexity”, Aaronson2011
  • “You’re Doing It Wrong: Think You’ve Mastered the Art of Server Performance? Think Again.”, Kamp2010
  • “Formal Theory of Creativity & Fun & Intrinsic Motivation (1990^–₂₀2010_14ya)”, Schmidhuber2010
  • “Coolex: The Coolest Way to Generate Combinations”, Ruskey & Williams2009
  • “De-Anonymizing Social Networks”, Narayanan & Shmatikov2009
  • “The Gödel Letter”, Gödel2009
  • “Producing Wrong Data Without Doing Anything Obviously Wrong!”, Mytkowicz et al 2009
  • “The Tactical Amulet Extraction Bot: Predicting and Controlling NetHack’s Randomness”
  • “Is There A Fourth Futamura Projection?”, Robert2009
  • “Dual-Pivot Quicksort Algorithm”, Yaroslavskiy2009
  • “Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes”, Schmidhuber2008
  • “Aggregating Inconsistent Information: Ranking and Clustering”, Ailon et al 2008
  • “Bigtable: A Distributed Storage System for Structured Data”, Chang et al 2008
  • “Interview With Donald Knuth”, Binstock2008
  • “Optimal Boarding Method for Airline Passengers”, Steffen2008
  • “Harnessing Vision for Computation”, Changizi2008
  • “Communication in Economic Mechanisms”, Segal2006b
  • “How To Break Anonymity of the Netflix Prize Dataset”, Narayanan & Shmatikov2006
  • “Global Multiple Objective Line Breaking”, Holkner2006
  • “Oral History of Butler Lampson § WWW”, Lampson & Kay2006 (page 36)
  • “History of Combinatorial Generation (The Art of Computer Programming: Volume 4: Pre-Fascicle 4B: §7.2.1.7) § Pg22”, Knuth2005 (page 22)
  • “Modeling Bursts and Heavy Tails in Human Dynamics”, Vazquez et al 2005
  • “NP-Complete Problems and Physical Reality”, Aaronson2005
  • “Lower-Stretch Spanning Trees”, Elkin et al 2004
  • “A Short History of Computational Complexity”, Fortnow et al 2003
  • “Least Effort and the Origins of Scaling in Human Language”, Cancho & Sole2003
  • “Extended Comment on Language Trees and Zipping”, Goodman2002
  • “Solving Real-World Linear Programs: A Decade and More of Progress”, Bixby2002
  • “A Bit-Vector Algorithm for Computing Levenshtein and Damerau Edit Distances”, Hyyrö2002
  • “Naked Objects: a Technique for Designing More Expressive Systems”, Pawson & Matthews2001
  • “On Proebsting’s Law”, Scott2001
  • “Peopleware: Why Measure Performance”, DeMarco & Lister2001
  • “The Effects of Moore’s Law and Slacking on Large Computations”, Gottbrath et al 1999
  • “Bridging the Algorithm Gap: A Linear-Time Functional Program for Paragraph Formatting”, Moor & Gibbons1999
  • “Feynman’s War: Modeling Weapons, Modeling Nature”, Galison1998
  • “The Concave Least-Weight Subsequence Problem Revisited”, Wilber1998
  • “Applications of Randomness in System Performance Measurement”, Blackwell1998
  • “The PageRank Citation Ranking: Bringing Order to the Web”, Page et al 1998
  • “Proebsting’s Law: Compiler Advances Double Computing Power Every 18 Years”, Proebsting1998
  • “The T-Experiments: Errors in Scientific Software”, Hatton1997
  • “George Prices’s Contributions to Evolutionary Genetics”, Frank1995
  • “The Nature of Selection”, Price1995
  • “A Plea for Lean Software”, Wirth1995
  • “Building a Large Annotated Corpus of English: The Penn Treebank”, Marcus et al 1993
  • “On the Computational Complexity of the Jones and Tutte Polynomials”, Jaeger et al 1990
  • “A Linear-Time Algorithm for Concave One-Dimensional Dynamic Programming”, Galil & Park1990
  • “Planning and Learning in Permutation Groups”, Mose et al 1989
  • “Separating Strings With Small Automata”, Robson1989
  • “Optimal Nonlinear Approximation”, DeVore et al 1989
  • “Hypertext and the Oxford English Dictionary”, Raymond & Tompa1988
  • Three Scientists and Their Gods: Looking for Meaning in an Age of Information, Wright1988
  • “Incentive Engineering: for Computational Resource Management”, Drexler & Miller1988
  • “The Least Weight Subsequence Problem”, Hirschberg & Larmore1987
  • “Geometric Applications of a Matrix Searching Algorithm”, Aggarwal et al 1986
  • “The Back of the Envelope Returns”, Bentley1986
  • “Average Case Complete Problems”, Levin1986
  • “Probabilistic Counting Algorithms for Data Base Applications”, Flajolet & Martin1985
  • “Programming As Theory Building”, Naur1985
  • “The Back of the Envelope”, Bentley1984
  • “The Competitive Allocation Process Is Informationally Efficient Uniquely”, Jordan1982
  • “Epigrams on Programming”, Perlis1982
  • “Procedural Reflection in Programming Languages”, Smith1982
  • “On Holy Wars and a Plea for Peace”, Cohen1981
  • “Mutation Analysis Of Program Test Data”, Budd1980
  • “A Correct Preprocessing Algorithm for Boyer-Moore String-Searching”, Rytter1980
  • “Algorithms for Loop Matchings”, Nussinov et al 1978
  • “Fast Pattern Matching in Strings”, Knuth et al 1977
  • “Structured Programming With go To Statements”, Knuth1974
  • Interstellar Communication: Scientific Perspectives, Ponnamperuma & Cameron1974
  • “A Parallel Algorithm for the Efficient Solution of a General Class of Recurrence Equations”, Kogge & Stone1973
  • “Universal Sequential Search Problems”, Levin1973
  • “The Dangers of Computer-Science Theory”, Knuth1973
  • “The Humble Programmer [EWD340]”, Dijkstra1972
  • “The Pattern of Streets”, Alexander1966
  • “A Simple Randomization Procedure”, Sandelius1962
  • “Generation of Random Permutations of Given Number of Elements Using Random Sampling Numbers”, Rao1961
  • “Addressing for Random-Access Storage”, Peterson1957
  • “The Codeless Code: Case 96: ‘Stateless’”
  • “Programmed Differently? Testing for Gender Differences in Python Programming Style and Quality on GitHub”
  • “Accidentally Quadratic”
  • “Summing ASCII Encoded Integers on Haswell at Almost the Speed of memcpy”
  • “We Need Visual Programming. No, Not like That.”
  • “Visualizing Algorithms”
  • “Personality Value”
  • “Measurement, Benchmarking, and Data Analysis Are Underrated”
  • “Dynamicland”, Victor2024
  • “Programmed Differently? Testing for Gender Differences in Python Programming Style and Quality on GitHub”
  • “Differentiable Finite State Machines”
  • “Getting the World Record in HATETRIS”
  • “LISP With GC in 436 Bytes”
  • “Heuristics on the High Seas: Mathematical Optimization for Cargo Ships”
  • “Programming’s Dirtiest Little Secret”, Yegg2024
  • “Submission #6347: Chef Stef’s NES Arkanoid warpless in 11:11.18”
  • “Differentiable Programming from Scratch”
  • “Technical Dimensions of Programming Systems”
  • “How Much of a Genius-Level Move Was Using Binary Space Partitioning in Doom?”
  • “An Open Letter to Netflix from the Authors of the De-Anonymization Paper”
  • “Scaling Our Spreadsheet Engine from Thousands to Billions of Cells”
  • “The Art and Mathematics of Genji-Kō”
  • “TSP Art”
  • Wikipedia
• Miscellaneous
• Bibliography