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2020 – today
- 2023
[c63]Monika Henzinger, Billy Jin, Richard Peng, David P. Williamson:
A Combinatorial Cut-Toggling Algorithm for Solving Laplacian Linear Systems. ITCS 2023: 69:1-69:22- [i77]Yuyang Xie, Jiezhong Qiu, Laxman Dhulipala, Wenjian Yu, Jie Tang, Richard Peng, Chi Wang:
Towards Lightweight and Automated Representation Learning System for Networks. CoRR abs/2302.07084 (2023) - [i76]Mehrdad Ghadiri, Richard Peng, Santosh S. Vempala:
The Bit Complexity of Efficient Continuous Optimization. CoRR abs/2304.02124 (2023) - 2022
- [c62]Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva:
Maximum Flow and Minimum-Cost Flow in Almost-Linear Time. FOCS 2022: 612-623 - [c61]Sally Dong, Yu Gao, Gramoz Goranci, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Guanghao Ye:
Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. SODA 2022: 124-153 - [c60]Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, Richard Peng, Aaron Sidford:
Faster maxflow via improved dynamic spectral vertex sparsifiers. STOC 2022: 543-556 - [c59]Richard Peng, Zhuoqing Song:
Sparsified block elimination for directed laplacians. STOC 2022: 557-567 - [i75]Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva:
Maximum Flow and Minimum-Cost Flow in Almost-Linear Time. CoRR abs/2203.00671 (2022) - [i74]Sally Dong, Yu Gao, Gramoz Goranci, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Guanghao Ye:
Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. CoRR abs/2205.01562 (2022) - [i73]Jingbang Chen, Yang P. Liu, Richard Peng, Arvind Ramaswami:
Exponential Convergence of Sinkhorn Under Regularization Scheduling. CoRR abs/2207.00736 (2022) - [i72]Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva:
Fast Algorithms for 𝓁p-Regression. CoRR abs/2211.03963 (2022) - 2021
- [c58]Yu Gao, Yang P. Liu, Richard Peng:
Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao. FOCS 2021: 516-527 - [c57]Li Chen, Richard Peng, Di Wang:
2-norm Flow Diffusion in Near-Linear Time. FOCS 2021: 540-549 - [c56]Sebastian Forster, Gramoz Goranci, Yang P. Liu, Richard Peng, Xiaorui Sun, Mingquan Ye:
Minor Sparsifiers and the Distributed Laplacian Paradigm. FOCS 2021: 989-999 - [c55]Jiezhong Qiu, Laxman Dhulipala, Jie Tang, Richard Peng, Chi Wang:
LightNE: A Lightweight Graph Processing System for Network Embedding. SIGMOD Conference 2021: 2281-2289 - [c54]Richard Peng, Santosh S. Vempala:
Solving Sparse Linear Systems Faster than Matrix Multiplication. SODA 2021: 504-521 - [c53]Parinya Chalermsook, Syamantak Das, Yunbum Kook, Bundit Laekhanukit, Yang P. Liu, Richard Peng, Mark Sellke, Daniel Vaz:
Vertex Sparsification for Edge Connectivity. SODA 2021: 1206-1225 - [i71]Yu Gao, Yang P. Liu, Richard Peng:
Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao. CoRR abs/2101.07233 (2021) - [i70]Li Chen, Richard Peng, Di Wang:
𝓁2-norm Flow Diffusion in Near-Linear Time. CoRR abs/2105.14629 (2021) - [i69]Monika Henzinger, Billy Jin, Richard Peng, David P. Williamson:
Cut-Toggling and Cycle-Toggling for Electrical Flow and Other p-Norm Flows. CoRR abs/2109.00653 (2021) - [i68]Mehrdad Ghadiri, Richard Peng, Santosh S. Vempala:
Sparse Regression Faster than dω. CoRR abs/2109.11537 (2021) - [i67]Jingbang Chen, Yu Gao, Yufan Huang, Richard Peng:
Unit-Weight Laplacians are Complete for Linear Systems Modulo $p$. CoRR abs/2109.12736 (2021) - [i66]Richard Peng, Zhuoqing Song:
Sparsified Block Elimination for Directed Laplacians. CoRR abs/2111.10257 (2021) - [i65]Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, Richard Peng, Aaron Sidford:
Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers. CoRR abs/2112.00722 (2021) - 2020
- [j8]David Durfee, John Peebles, Richard Peng, Anup B. Rao:
Determinant-Preserving Sparsification of SDDM Matrices. SIAM J. Comput. 49(4) (2020) - [c52]Jan van den Brand
, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang:
Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs. FOCS 2020: 919-930 - [c51]Li Chen, Gramoz Goranci, Monika Henzinger, Richard Peng, Thatchaphol Saranurak:
Fast Dynamic Cuts, Distances and Effective Resistances via Vertex Sparsifiers. FOCS 2020: 1135-1146 - [c50]Julia Chuzhoy, Yu Gao, Jason Li, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak:
A Deterministic Algorithm for Balanced Cut with Applications to Dynamic Connectivity, Flows, and Beyond. FOCS 2020: 1158-1167 - [c49]Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang:
Faster Graph Embeddings via Coarsening. ICML 2020: 2953-2963 - [c48]Jiezhong Qiu, Chi Wang, Ben Liao, Richard Peng, Jie Tang:
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices. NeurIPS 2020 - [c47]Laxman Dhulipala, David Durfee, Janardhan Kulkarni, Richard Peng, Saurabh Sawlani, Xiaorui Sun:
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds. SODA 2020: 1300-1319 - [c46]Digvijay Boob, Yu Gao, Richard Peng, Saurabh Sawlani, Charalampos E. Tsourakakis, Di Wang, Junxing Wang:
Flowless: Extracting Densest Subgraphs Without Flow Computations. WWW 2020: 573-583 - [i64]Yihe Dong, Yu Gao, Richard Peng, Ilya P. Razenshteyn, Saurabh Sawlani:
A Study of Performance of Optimal Transport. CoRR abs/2005.01182 (2020) - [i63]Li Chen, Gramoz Goranci, Monika Henzinger, Richard Peng, Thatchaphol Saranurak:
Fast Dynamic Cuts, Distances and Effective Resistances via Vertex Sparsifiers. CoRR abs/2005.02368 (2020) - [i62]Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang:
Faster Graph Embeddings via Coarsening. CoRR abs/2007.02817 (2020) - [i61]Parinya Chalermsook, Syamantak Das, Bundit Laekhanukit, Yunbum Kook, Yang P. Liu, Richard Peng, Mark Sellke, Daniel Vaz:
Vertex Sparsification for Edge Connectivity. CoRR abs/2007.07862 (2020) - [i60]Richard Peng, Santosh S. Vempala:
Solving Sparse Linear Systems Faster than Matrix Multiplication. CoRR abs/2007.10254 (2020) - [i59]Jiezhong Qiu, Chi Wang, Ben Liao, Richard Peng, Jie Tang:
Concentration Bounds for Co-occurrence Matrices of Markov Chains. CoRR abs/2008.02464 (2020) - [i58]Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang:
Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs. CoRR abs/2009.01802 (2020) - [i57]Sebastian Forster, Gramoz Goranci, Yang P. Liu, Richard Peng, Xiaorui Sun, Mingquan Ye:
Minor Sparsifiers and the Distributed Laplacian Paradigm. CoRR abs/2012.15675 (2020)
2010 – 2019
- 2019
- [c45]Deeksha Adil, Richard Peng, Sushant Sachdeva:
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression. NeurIPS 2019: 14166-14177 - [c44]Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva:
Iterative Refinement for ℓp-norm Regression. SODA 2019: 1405-1424 - [c43]Rasmus Kyng, Richard Peng, Sushant Sachdeva, Di Wang:
Flows in almost linear time via adaptive preconditioning. STOC 2019: 902-913 - [c42]David Durfee, Yu Gao, Gramoz Goranci, Richard Peng:
Fully dynamic spectral vertex sparsifiers and applications. STOC 2019: 914-925 - [c41]Richard Peng, Bryce Sandlund, Daniel Dominic Sleator:
Optimal Offline Dynamic 2, 3-Edge/Vertex Connectivity. WADS 2019: 553-565 - [c40]Huan Li, Richard Peng, Liren Shan, Yuhao Yi, Zhongzhi Zhang:
Current Flow Group Closeness Centrality for Complex Networks? WWW 2019: 961-971 - [i56]Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva:
Iterative Refinement for 𝓁p-norm Regression. CoRR abs/1901.06764 (2019) - [i55]Brian Bullins, Richard Peng:
Higher-Order Accelerated Methods for Faster Non-Smooth Optimization. CoRR abs/1906.01621 (2019) - [i54]Rasmus Kyng, Richard Peng, Sushant Sachdeva, Di Wang:
Flows in Almost Linear Time via Adaptive Preconditioning. CoRR abs/1906.10340 (2019) - [i53]David Durfee, Yu Gao, Gramoz Goranci, Richard Peng:
Fully Dynamic Spectral Vertex Sparsifiers and Applications. CoRR abs/1906.10530 (2019) - [i52]Deeksha Adil, Richard Peng, Sushant Sachdeva:
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression. CoRR abs/1907.07167 (2019) - [i51]J. Ian Munro, Richard Peng, Sebastian Wild, Lingyi Zhang:
Dynamic Optimality Refuted - For Tournament Heaps. CoRR abs/1908.00563 (2019) - [i50]David Durfee, Laxman Dhulipala, Janardhan Kulkarni, Richard Peng, Saurabh Sawlani, Xiaorui Sun:
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds. CoRR abs/1908.01956 (2019) - [i49]Digvijay Boob, Yu Gao, Richard Peng, Saurabh Sawlani, Charalampos E. Tsourakakis, Di Wang, Junxing Wang:
Flowless: Extracting Densest Subgraphs Without Flow Computations. CoRR abs/1910.07087 (2019) - [i48]Yu Gao, Jason Li, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Sorrachai Yingchareonthawornchai:
Deterministic Graph Cuts in Subquadratic Time: Sparse, Balanced, and k-Vertex. CoRR abs/1910.07950 (2019) - [i47]Julia Chuzhoy, Yu Gao, Jason Li, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak:
A Deterministic Algorithm for Balanced Cut with Applications to Dynamic Connectivity, Flows, and Beyond. CoRR abs/1910.08025 (2019) - [i46]Yang P. Liu, Richard Peng, Mark Sellke:
Vertex Sparsifiers for c-Edge Connectivity. CoRR abs/1910.10359 (2019) - 2018
- [c39]Meng He, Richard Peng, Yinzhan Xu:
Parameterizing the Hardness of Binary Search Tree Access Sequences by Inversion Counts. ANALCO 2018: 32-39 - [c38]Matthew Fahrbach, Gary L. Miller, Richard Peng, Saurabh Sawlani, Junxing Wang, Shen Chen Xu:
Graph Sketching against Adaptive Adversaries Applied to the Minimum Degree Algorithm. FOCS 2018: 101-112 - [c37]Timothy Chu, Yu Gao, Richard Peng, Sushant Sachdeva, Saurabh Sawlani, Junxing Wang:
Graph Sparsification, Spectral Sketches, and Faster Resistance Computation, via Short Cycle Decompositions. FOCS 2018: 361-372 - [c36]Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford:
Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations. FOCS 2018: 898-909 - [c35]Rasmus Kyng, Richard Peng, Robert Schwieterman, Peng Zhang:
Incomplete nested dissection. STOC 2018: 404-417 - [i45]Huan Li, Richard Peng, Liren Shan, Yuhao Yi, Zhongzhi Zhang:
Current Flow Group Closeness Centrality for Complex Networks. CoRR abs/1802.02556 (2018) - [i44]David Durfee, Yu Gao, Gramoz Goranci, Richard Peng:
Fully Dynamic Effective Resistances. CoRR abs/1804.04038 (2018) - [i43]Matthew Fahrbach, Gary L. Miller, Richard Peng, Saurabh Sawlani, Junxing Wang, Shen Chen Xu:
Graph Sketching Against Adaptive Adversaries Applied to the Minimum Degree Algorithm. CoRR abs/1804.04239 (2018) - [i42]Rasmus Kyng, Richard Peng, Robert Schwieterman, Peng Zhang:
Incomplete Nested Dissection. CoRR abs/1805.09442 (2018) - [i41]Timothy Chu, Yu Gao, Richard Peng, Sushant Sachdeva, Saurabh Sawlani, Junxing Wang:
Graph Sparsification, Spectral Sketches, and Faster Resistance Computation, via Short Cycle Decompositions. CoRR abs/1805.12051 (2018) - [i40]Timothy Chu, Michael B. Cohen, Jakub W. Pachocki, Richard Peng:
Constant Arboricity Spectral Sparsifiers. CoRR abs/1808.05662 (2018) - [i39]Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford:
Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations. CoRR abs/1811.10722 (2018) - [i38]Henning Meyerhenke, Richard Peng, Ilya Safro:
High-Performance Graph Algorithms (Dagstuhl Seminar 18241). Dagstuhl Reports 8(6): 19-39 (2018) - 2017
- [j7]Richard Peng, He Sun, Luca Zanetti:
Partitioning Well-Clustered Graphs: Spectral Clustering Works! SIAM J. Comput. 46(2): 710-743 (2017) - [c34]Gorav Jindal, Pavel Kolev, Richard Peng, Saurabh Sawlani:
Density Independent Algorithms for Sparsifying k-Step Random Walks. APPROX-RANDOM 2017: 14:1-14:17 - [c33]David Durfee, John Peebles, Richard Peng, Anup B. Rao:
Determinant-Preserving Sparsification of SDDM Matrices with Applications to Counting and Sampling Spanning Trees. FOCS 2017: 926-937 - [c32]Rasmus Kyng, Jakub Pachocki, Richard Peng, Sushant Sachdeva:
A Framework for Analyzing Resparsification Algorithms. SODA 2017: 2032-2043 - [c31]Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford, Adrian Vladu:
Almost-linear-time algorithms for Markov chains and new spectral primitives for directed graphs. STOC 2017: 410-419 - [i37]Gorav Jindal, Pavel Kolev, Richard Peng, Saurabh Sawlani:
Density Independent Algorithms for Sparsifying k-Step Random Walks. CoRR abs/1702.06110 (2017) - [i36]Tung Mai, Richard Peng, Anup B. Rao, Vijay V. Vazirani:
Concave Flow on Small Depth Directed Networks. CoRR abs/1704.07791 (2017) - [i35]David Durfee, John Peebles, Richard Peng, Anup B. Rao:
Determinant-Preserving Sparsification of SDDM Matrices with Applications to Counting and Sampling Spanning Trees. CoRR abs/1705.00985 (2017) - [i34]Richard Peng, Bryce Sandlund, Daniel Dominic Sleator:
Offline Dynamic Higher Connectivity. CoRR abs/1708.03812 (2017) - [i33]Matthew Fahrbach, Gary L. Miller, Richard Peng, Saurabh Sawlani, Junxing Wang, Shen Chen Xu:
On Computing Min-Degree Elimination Orderings. CoRR abs/1711.08446 (2017) - 2016
- [j6]Ioannis Koutis, Alex Levin, Richard Peng:
Faster Spectral Sparsification and Numerical Algorithms for SDD Matrices. ACM Trans. Algorithms 12(2): 17:1-17:16 (2016) - [c30]Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla, Gary L. Miller, Richard Peng:
Simple and Scalable Constrained Clustering: a Generalized Spectral Method. AISTATS 2016: 445-454 - [c29]Ittai Abraham, David Durfee, Ioannis Koutis, Sebastian Krinninger, Richard Peng:
On Fully Dynamic Graph Sparsifiers. FOCS 2016: 335-344 - [c28]Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Aaron Sidford, Adrian Vladu:
Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More. FOCS 2016: 583-592 - [c27]Dehua Cheng, Richard Peng, Yan Liu, Ioakeim Perros:
SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling. NIPS 2016: 721-729 - [c26]Kevin Deweese, John R. Gilbert, Gary L. Miller, Richard Peng, Hao Ran Xu, Shen Chen Xu:
An Empirical Study of Cycle Toggling Based Laplacian Solvers. CSC 2016: 33-41 - [c25]Richard Peng:
Approximate Undirected Maximum Flows in O(mpolylog(n)) Time. SODA 2016: 1862-1867 - [c24]Rasmus Kyng, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Daniel A. Spielman:
Sparsified Cholesky and multigrid solvers for connection laplacians. STOC 2016: 842-850 - [i32]Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla, Gary L. Miller, Richard Peng:
Scalable Constrained Clustering: A Generalized Spectral Method. CoRR abs/1601.04746 (2016) - [i31]Ittai Abraham, David Durfee, Ioannis Koutis, Sebastian Krinninger, Richard Peng:
On Fully Dynamic Graph Sparsifiers. CoRR abs/1604.02094 (2016) - [i30]Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Aaron Sidford, Adrian Vladu:
Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More. CoRR abs/1608.03270 (2016) - [i29]Kevin Deweese, John R. Gilbert, Gary L. Miller, Richard Peng, Hao Ran Xu, Shen Chen Xu:
An Empirical Study of Cycle Toggling Based Laplacian Solvers. CoRR abs/1609.02957 (2016) - [i28]Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford, Adrian Vladu:
Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs. CoRR abs/1611.00755 (2016) - [i27]Rasmus Kyng, Jakub Pachocki, Richard Peng, Sushant Sachdeva:
A Framework for Analyzing Resparsification Algorithms. CoRR abs/1611.06940 (2016) - 2015
- [c23]Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng:
Efficient Sampling for Gaussian Graphical Models via Spectral Sparsification. COLT 2015: 364-390 - [c22]Richard Peng, He Sun, Luca Zanetti:
Partitioning Well-Clustered Graphs: Spectral Clustering Works! COLT 2015: 1423-1455 - [c21]Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, Aaron Sidford:
Uniform Sampling for Matrix Approximation. ITCS 2015: 181-190 - [c20]Michael Mitzenmacher, Jakub Pachocki, Richard Peng, Charalampos E. Tsourakakis, Shen Chen Xu:
Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling. KDD 2015: 815-824 - [c19]Gary L. Miller, Richard Peng, Adrian Vladu, Shen Chen Xu:
Improved Parallel Algorithms for Spanners and Hopsets. SPAA 2015: 192-201 - [c18]Michael B. Cohen, Richard Peng:
Lp Row Sampling by Lewis Weights. STOC 2015: 183-192 - [i26]Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng:
Spectral Sparsification of Random-Walk Matrix Polynomials. CoRR abs/1502.03496 (2015) - [i25]Yin Tat Lee, Richard Peng, Daniel A. Spielman:
Sparsified Cholesky Solvers for SDD linear systems. CoRR abs/1506.08204 (2015) - [i24]Rasmus Kyng, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Daniel A. Spielman:
Sparsified Cholesky and Multigrid Solvers for Connection Laplacians. CoRR abs/1512.01892 (2015) - 2014
- [j5]Guy E. Blelloch, Anupam Gupta, Ioannis Koutis, Gary L. Miller, Richard Peng, Kanat Tangwongsan:
Nearly-Linear Work Parallel SDD Solvers, Low-Diameter Decomposition, and Low-Stretch Subgraphs. Theory Comput. Syst. 55(3): 521-554 (2014) - [j4]Ioannis Koutis, Gary L. Miller, Richard Peng:
Approaching Optimality for Solving SDD Linear Systems. SIAM J. Comput. 43(1): 337-354 (2014) - [c17]Michael B. Cohen, Brittany Terese Fasy, Gary L. Miller, Amir Nayyeri, Richard Peng, Noel Walkington:
Solving 1-Laplacians in Nearly Linear Time: Collapsing and Expanding a Topological Ball. SODA 2014: 204-216 - [c16]Richard Peng, Daniel A. Spielman:
An efficient parallel solver for SDD linear systems. STOC 2014: 333-342 - [c15]Michael B. Cohen, Rasmus Kyng, Gary L. Miller, Jakub W. Pachocki, Richard Peng, Anup B. Rao, Shen Chen Xu:
Solving SDD linear systems in nearly mlog1/2n time. STOC 2014: 343-352 - [i23]Michael B. Cohen, Gary L. Miller, Jakub W. Pachocki, Richard Peng, Shen Chen Xu:
Stretching Stretch. CoRR abs/1401.2454 (2014) - [i22]Michael B. Cohen, Rasmus Kyng, Jakub W. Pachocki, Richard Peng, Anup B. Rao:
Preconditioning in Expectation. CoRR abs/1401.6236 (2014) - [i21]Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, Aaron Sidford:
Uniform Sampling for Matrix Approximation. CoRR abs/1408.5099 (2014) - [i20]Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng:
Scalable Parallel Factorizations of SDD Matrices and Efficient Sampling for Gaussian Graphical Models. CoRR abs/1410.5392 (2014) - [i19]Richard Peng, He Sun, Luca Zanetti:
Partitioning Well-Clustered Graphs with k-Means and Heat Kernel. CoRR abs/1411.2021 (2014) - [i18]Richard Peng:
A Note on Cut-Approximators and Approximating Undirected Max Flows. CoRR abs/1411.7631 (2014) - [i17]Michael B. Cohen, Richard Peng:
ℓp Row Sampling by Lewis Weights. CoRR abs/1412.0588 (2014) - [i16]Ioannis Koutis, Gary L. Miller, Richard Peng:
A Generalized Cheeger Inequality. CoRR abs/1412.6075 (2014) - 2013
- [b1]Richard Peng:
Algorithm Design Using Spectral Graph Theory. Carnegie Mellon University, USA, 2013 - [c14]Mu Li, Gary L. Miller, Richard Peng:
Iterative Row Sampling. FOCS 2013: 127-136 - [c13]Manoj Gupta, Richard Peng:
Fully Dynamic (1+ e)-Approximate Matchings. FOCS 2013: 548-557 - [c12]Hui Han Chin, Aleksander Madry, Gary L. Miller, Richard Peng:
Runtime guarantees for regression problems. ITCS 2013: 269-282 - [c11]Gary L. Miller, Richard Peng:
Approximate Maximum Flow on Separable Undirected Graphs. SODA 2013: 1151-1170 - [c10]Gary L. Miller, Richard Peng, Shen Chen Xu:
Parallel graph decompositions using random shifts. SPAA 2013: 196-203 - [i15]Manoj Gupta, Richard Peng:
Fully Dynamic $(1+ε)$-Approximate Matchings. CoRR abs/1304.0378 (2013) - [i14]Gary L. Miller, Richard Peng, Shen Chen Xu:
Parallel Graph Decompositions Using Random Shifts. CoRR abs/1307.3692 (2013) - [i13]Gary L. Miller, Richard Peng, Shen Chen Xu:
Parallel Algorithms for Approximate Undirected Shortest Paths in $m\log^{3+α}n$ Work. CoRR abs/1309.3545 (2013) - [i12]Richard Peng, Daniel A. Spielman:
An Efficient Parallel Solver for SDD Linear Systems. CoRR abs/1311.3286 (2013) - 2012
- [j3]Ioannis Koutis, Gary L. Miller, Richard Peng:
A fast solver for a class of linear systems. Commun. ACM 55(10): 99-107 (2012)