Search Results - approximate statistics (dynamic OR dynamika) programming algorithm*

Establishment and Solution of a Multi-Stage Decision Model Based on Hypothesis Testing and Dynamic Programming Algorithm

Authors: Liu, Ziyang Hu, Yurui Deng, Yihan

Source: 2024 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC). :884-889

Subject Terms: FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, Applications (stat.AP), Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Statistics - Applications

Access URL: http://arxiv.org/abs/2503.05807

Constrained many-to-many point matching in two dimensions

Authors: L.E. Caraballo R. A. Castro José Miguel Díaz-Báñez et al.

Contributors: L.E. Caraballo R. A. Castro José Miguel Díaz-Báñez et al.

Source: idUS. Depósito de Investigación de la Universidad de Sevilla
Universidad de Sevilla (US)

Subject Terms: Combinatorics on Words, Linguistics and Language, Artificial intelligence, Automata Theory and Formal Languages, Approximate Matching, Social Sciences, Geometry, 0102 computer and information sciences, 02 engineering and technology, Dynamic programming, 7. Clean energy, 01 natural sciences, Point (geometry), Artificial Intelligence, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Computational intelligence, Melodic similarity, Text Compression and Indexing Algorithms, 4. Education, Statistics, Geometric measure, 16. Peace & justice, Computer science, Computational Theory and Mathematics, Computer Science, Physical Sciences, Matching (statistics), String Matching, Many-to-many matching, Translanguaging Pedagogy in Multilingual Education, Mathematics

File Description: application/pdf

Access URL: https://hdl.handle.net/11441/155627
https://idus.us.es/handle//11441/155627

Real-Time Scheduling Optimization of Integrated Energy Systems in Smart Grids based on Approximate Dynamic Programming

Authors: Dongzhao Wang Yue Sun Yan Wu et al.

Source: Statistics, Optimization & Information Computing. 13:158-172

An Approximate Dynamic Programming Algorithm for Large-Scale Fleet Management: A Case Application

Authors: Simão, Hugo P. Day, Jeff George, Abraham P. et al.

Source: Transportation Science, 2009 May 01. 43(2), 178-197.

Access URL: https://www.jstor.org/stable/25769444

Modeling Dynamic Regime Shifts in Diffusion Processes: Approximate Maximum Likelihood Estimation for Two-Threshold Ornstein–Uhlenbeck Models.

Authors: Bekesiene, Svajone Nikitin, Anatolii Nechyporuk, Serhii

Source: Mathematics (2227-7390). Nov2025, Vol. 13 Issue 21, p3450. 18p.

Subject Terms: *ORNSTEIN-Uhlenbeck process, *PARAMETER estimation, *STOCHASTIC processes, *PARAMETERIZATION, *DIFFUSION kinetics, *STOCHASTIC models, *ITERATIVE methods (Mathematics), *MAXIMUM likelihood statistics

A fully adaptive framework for continuous-state stochastic dynamic programming.

Authors: Fan, Huiyuan Tarun, Prashant K. Viswanatha, Amith et al.

Source: Computers & Operations Research. Nov2025, Vol. 183, pN.PAG-N.PAG. 1p.

Subject Terms: *DYNAMIC programming, *STATISTICS, STATISTICAL models, SPACE exploration, COMPUTER engineering

Exact and rapid linear clustering of networks with dynamic programming

Authors: Alice Patania Antoine Allard Jean-Gabriel Young

Source: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 479

Subject Terms: Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, 0103 physical sciences, Computer Science - Social and Information Networks, Data Structures and Algorithms (cs.DS), Statistics - Computation, 01 natural sciences, Computation (stat.CO), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2301.10403

Deep Neural Networks Algorithms for Stochastic Control Problems on Finite Horizon: Convergence Analysis

Authors: Côme Huré Huyên Pham Achref Bachouch et al.

Contributors: Côme Huré Huyên Pham Achref Bachouch et al.

Source: SIAM Journal on Numerical Analysis. 59:525-557

Subject Terms: dynamic programming, FOS: Computer and information sciences, 90C39, [MATH.MATH-PR] Mathematics [math]/Probability [math.PR], Probability (math.PR), statistical risk AMS subject classifications. 65C05, Deep learning, [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], Machine Learning (stat.ML), performance iteration, 93E35, 01 natural sciences, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], convergence analysis, Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, regress now, quantization, 0101 mathematics, Mathematics - Optimization and Control, 65C05, 90C39, 93E35, 68T07, Mathematics - Probability

File Description: application/pdf

Access URL: http://arxiv.org/pdf/1812.04300
http://arxiv.org/abs/1812.04300
https://arxiv.org/pdf/1812.04300.pdf
https://ui.adsabs.harvard.edu/abs/2018arXiv181204300H/abstract
http://ui.adsabs.harvard.edu/abs/2018arXiv181204300H/abstract
https://arxiv.org/abs/1812.04300
https://dblp.uni-trier.de/db/journals/siamnum/siamnum59.html#HurePBL21
https://hal.archives-ouvertes.fr/hal-01949213/document
https://hal.archives-ouvertes.fr/hal-01949213v2
https://epubs.siam.org/doi/abs/10.1137/20M1316640
https://hal.science/hal-01949213v2
https://hal.science/hal-01949213v2/document
https://doi.org/10.1137/20m1316640

Dynamic programming based optimized product quantization for approximate nearest neighbor search.

Authors: Cai, Yuanzheng Ji, Rongrong Li, Shaozi

Source: Neurocomputing. Dec2016, Vol. 217, p110-118. 9p.

Subject Terms: *DYNAMIC programming, *NEAREST neighbor analysis (Statistics), *SUBSPACES (Mathematics), *ALGORITHMS, *GRAPH theory

An Approximate Dynamic Programming Approach to Adversarial Online Learning

Authors: Kamble, Vijay Loiseau, Patrick Walrand, Jean

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computer Science and Game Theory, Statistics - Machine Learning, Computer Science - Data Structures and Algorithms, 0502 economics and business, 05 social sciences, Data Structures and Algorithms (cs.DS), Machine Learning (stat.ML), 0102 computer and information sciences, 01 natural sciences, Computer Science and Game Theory (cs.GT), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/1603.04981

Online multivariate changepoint detection: leveraging links with computational geometry

Authors: Pishchagina, Liudmila Romano, Gaetano Fearnhead, Paul et al.

Contributors: Pishchagina, Liudmila Romano, Gaetano Fearnhead, Paul et al.

Source: Journal of the Royal Statistical Society Series B: Statistical Methodology.

Subject Terms: dynamic programming, FOS: Computer and information sciences, online changepoint detection, Computation, computational geometry, convex hull, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Computation (stat.CO)

File Description: text

Access URL: http://arxiv.org/abs/2311.01174
https://hal.inrae.fr/hal-05239757v1
https://doi.org/10.1093/jrsssb/qkaf046
https://eprints.lancs.ac.uk/id/eprint/209818/

Equilibria of large random Lotka-Volterra systems with vanishing species: a mathematical approach.

Authors: Akjouj I Hachem W Maïda M et al.

Source: Journal of mathematical biology [J Math Biol] 2024 Nov 07; Vol. 89 (6), pp. 61. Date of Electronic Publication: 2024 Nov 07.

Publication Type: Journal Article

Journal Info: Publisher: Springer Verlag Country of Publication: Germany NLM ID: 7502105 Publication Model: Electronic Cited Medium: Internet ISSN: 1432-1416 (Electronic) Linking ISSN: 03036812 NLM ISO Abbreviation: J Math Biol Subsets: MEDLINE

MeSH Terms: Algorithms* , Mathematical Concepts* , Ecosystem* , Models, Biological* , Population Dynamics*/statistics & numerical data, Animals ; Linear Models ; Computer Simulation ; Models, Statistical

State abstraction discovery from progressive disaggregation methods

Authors: Forghieri, Orso Castel, Hind Hyon, Emmanuel et al.

Contributors: Forghieri, Orso Castel, Hind Hyon, Emmanuel et al.

Source: EWLR 2024: 17th European Workshop on Reinforcement Learning ; 17th European Workshop on Reinforcement Learning (EWLR) ; https://hal.science/hal-05004125 ; 17th European Workshop on Reinforcement Learning (EWLR), Oct 2024, Toulouse, France

Subject Terms: Markov decision Processes, Reinforcement Learning, State abstraction, Approximate Dynamic Programming, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]

Subject Geographic: Toulouse, France

Availability: https://hal.science/hal-05004125
https://hal.science/hal-05004125v1/document
https://hal.science/hal-05004125v1/file/56_State_Abstraction_Discovery.pdf

Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees.

Authors: Zeng, Siliang Hong, Mingyi Garcia, Alfredo

Source: Operations Research. Mar/Apr2025, Vol. 73 Issue 2, p720-737. 18p.

Subject Terms: *DISCRETE choice models, *COMPUTER programming, *MARKOV processes, MAXIMUM likelihood statistics, REINFORCEMENT learning

A New Optimal Stepsize for Approximate Dynamic Programming

Authors: Peter I. Frazier Ilya O. Ryzhov Warren B. Powell

Source: IEEE Transactions on Automatic Control. 60:743-758

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, 0209 industrial biotechnology, Computer Science - Artificial Intelligence, 0211 other engineering and technologies, Machine Learning (stat.ML), Systems and Control (eess.SY), 02 engineering and technology, Electrical Engineering and Systems Science - Systems and Control, 7. Clean energy, Machine Learning (cs.LG), 3. Good health, Artificial Intelligence (cs.AI), Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, FOS: Electrical engineering, electronic engineering, information engineering, Mathematics - Optimization and Control

Access URL: http://arxiv.org/pdf/1407.2676.pdf
http://arxiv.org/abs/1407.2676
https://castlelab.princeton.edu/Papers/Ryzhov-OptimalStepsizeforADPFeb242015.pdf
http://castlelab.princeton.edu/Papers/Ryzhov-OptimalStepsizeforADPFeb242015.pdf
http://dblp.uni-trier.de/db/journals/corr/corr1407.html#RyzhovFP14
https://doi.org/10.1109/TAC.2014.2357134
http://ieeexplore.ieee.org/document/6897935/
https://ieeexplore.ieee.org/document/6897935

An Approximate Dynamic Programming Algorithm for Large-Scale Fleet Management: A Case Application

Authors: John Nienow Jeff Day Hugo P. Simão et al.

Source: Transportation Science. 43:178-197

Subject Terms: 0502 economics and business, 05 social sciences, 11. Sustainability, 0211 other engineering and technologies, 02 engineering and technology

Efficient approximate algorithms for a class of dynamic lot size problems under product substitution.

Authors: Huang, Shanming Xue, Feng Yang, Wanshuo

Source: Journal of Industrial & Management Optimization. Jan2024, Vol. 20 Issue 1, p1-24. 24p.

Subject Terms: *DYNAMIC programming, *ALGORITHMS, *PROBLEM solving, POLYNOMIAL time algorithms, BIPARTITE graphs

EFFICIENT NEURAL NETWORK APPROACHES FOR CONDITIONAL OPTIMAL TRANSPORT WITH APPLICATIONS IN BAYESIAN INFERENCE.

Authors: ZHEYU OLIVER WANG BAPTISTA, RICARDO MARZOUK, YOUSSEF et al.

Source: SIAM Journal on Scientific Computing. 2025, Vol. 47 Issue 4, pC979-C1005. 27p.

Subject Terms: *ARTIFICIAL neural networks, *BAYESIAN field theory, *DYNAMIC programming, *STATISTICAL sampling, *MATHEMATICAL optimization, *NUMERICAL analysis, *MAXIMUM likelihood statistics

Good Prophets Know When the End Is Near.

Authors: Banerjee, Siddhartha Freund, Daniel

Source: Management Science. Jun2025, Vol. 71 Issue 6, p4877-4894. 18p.

Subject Terms: *BIN packing problem, *MATHEMATICAL optimization, *STOCHASTIC processes, PREDICTIVE control systems, LOSS functions (Statistics)

A NONPARAMETRIC ROBUST OPTIMIZATION APPROACH FOR CHANCE-CONSTRAINED KNAPSACK PROBLEM.

Authors: XU, LIANG ZHANG, CHAO XU, ZHOU et al.

Source: SIAM Journal on Optimization. 2025, Vol. 35 Issue 2, p739-766. 28p.

Subject Terms: *ROBUST optimization, *KNAPSACK problems, *RANDOM sets, *DYNAMIC programming, *OPTIMIZATION algorithms, *NONPARAMETRIC statistics, *STOCHASTIC programming, *COMBINATORIAL optimization