Iteratively reweighted least squares and slime mold dynamics: connection and convergence
暂无分享,去创建一个
[1] Shang-Hua Teng,et al. Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems , 2003, STOC '04.
[2] Kurt Mehlhorn,et al. Physarum can compute shortest paths , 2011, SODA.
[3] Andrew V. Goldberg,et al. Beyond the flow decomposition barrier , 1998, JACM.
[4] Kurt Mehlhorn,et al. Convergence of the Non-Uniform Directed Physarum Model , 2019, Theor. Comput. Sci..
[5] Nisheeth K. Vishnoi,et al. On a Natural Dynamics for Linear Programming , 2015, ITCS.
[6] M. Putti,et al. Numerical Solution of Monge-Kantorovich Equations via a dynamic formulation , 2017, 1709.06765.
[7] Bernard Chazelle,et al. Natural algorithms and influence systems , 2012, CACM.
[8] Luca Cardelli,et al. The Cell Cycle Switch Computes Approximate Majority , 2012, Scientific Reports.
[9] Jack Brimberg,et al. Global Convergence of a Generalized Iterative Procedure for the Minisum Location Problem with lp Distances , 1993, Oper. Res..
[10] Nisheeth K. Vishnoi. The Speed of Evolution , 2015, SODA.
[11] Yin Tat Lee,et al. An homotopy method for lp regression provably beyond self-concordance and in input-sparsity time , 2018, STOC.
[12] Shang-Hua Teng,et al. The Laplacian Paradigm: Emerging Algorithms for Massive Graphs , 2010, TAMC.
[13] Robert E. Tarjan,et al. Network Flow and Testing Graph Connectivity , 1975, SIAM J. Comput..
[14] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[15] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[16] Noga Alon,et al. A Biological Solution to a Fundamental Distributed Computing Problem , 2011, Science.
[17] Richard M. Karp,et al. A n^5/2 Algorithm for Maximum Matchings in Bipartite Graphs , 1971, SWAT.
[18] Jonah Sherman,et al. Nearly Maximum Flows in Nearly Linear Time , 2013, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science.
[19] L. Perko. Differential Equations and Dynamical Systems , 1991 .
[20] Prateek Jain,et al. Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems , 2019, AISTATS.
[21] Deborah M. Gordon,et al. Ant Encounters: Interaction Networks and Colony Behavior , 2010 .
[22] Yin Tat Lee,et al. Path Finding Methods for Linear Programming: Solving Linear Programs in Õ(vrank) Iterations and Faster Algorithms for Maximum Flow , 2014, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science.
[23] I. Daubechies,et al. Iteratively reweighted least squares minimization for sparse recovery , 2008, 0807.0575.
[24] J. Haladyn. Comb , 2019, Duchamp, Aesthetics and Capitalism.
[25] Daniel A. Spielman,et al. Faster approximate lossy generalized flow via interior point algorithms , 2008, STOC.
[26] Liming Yang,et al. Iteratively reweighted least squares for robust regression via SVM and ELM , 2019, ArXiv.
[27] Junzhou Huang,et al. Fast iteratively reweighted least squares algorithms for analysis‐based sparse reconstruction , 2018, Medical Image Anal..
[28] Stephen J. Wright. Primal-Dual Interior-Point Methods , 1997, Other Titles in Applied Mathematics.
[29] Kurt Mehlhorn,et al. Two Results on Slime Mold Computations , 2019, Theor. Comput. Sci..
[30] M. Sampson,et al. Medical students’ perception of lesbian, gay, bisexual, and transgender (LGBT) discrimination in their learning environment and their self-reported comfort level for caring for LGBT patients: a survey study , 2017, Medical education online.
[31] Nisheeth K. Vishnoi,et al. Natural Algorithms for Flow Problems , 2016, SODA.
[32] Daniel A. Spielman. Algorithms, Graph Theory, and the Solution of Laplacian Linear Equations , 2012, ICALP.
[33] Nisheeth K. Vishnoi,et al. Approximating the exponential, the lanczos method and an Õ(m)-time spectral algorithm for balanced separator , 2011, STOC '12.
[34] J. McClellan,et al. Complex Chebyshev approximation for FIR filter design , 1995 .
[35] Adrian Vladu,et al. Improved Convergence for and 1 Regression via Iteratively Reweighted Least Squares , 2019 .
[36] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[37] A. Tero,et al. A mathematical model for adaptive transport network in path finding by true slime mold. , 2007, Journal of theoretical biology.
[38] Kurt Mehlhorn,et al. Physarum Can Compute Shortest Paths: Convergence Proofs and Complexity Bounds , 2013, ICALP.
[39] S. Thomas Alexander,et al. A relationship between the recursive least squares update and homotopy continuation methods , 1991, IEEE Trans. Signal Process..
[40] M. R. Osborne. Finite Algorithms in Optimization and Data Analysis , 1985 .
[41] Bhaskar D. Rao,et al. Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..
[42] Richard Peng,et al. Iterative Refinement for ℓp-norm Regression , 2019, SODA.
[43] C. Burrus. Iterative Reweighted Least Squares ∗ , 2014 .
[44] Amir Beck,et al. On the Convergence of Alternating Minimization for Convex Programming with Applications to Iteratively Reweighted Least Squares and Decomposition Schemes , 2015, SIAM J. Optim..
[45] Kurt Mehlhorn,et al. Convergence of the Non-Uniform Physarum Dynamics , 2019, Theor. Comput. Sci..
[46] Richard Peng,et al. Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression , 2019, NeurIPS.
[47] Nisheeth K. Vishnoi,et al. Lx = b , 2013, Found. Trends Theor. Comput. Sci..
[48] Wotao Yin,et al. Iteratively reweighted algorithms for compressive sensing , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[49] Michael Elad,et al. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[50] Leslie G. Valiant,et al. Evolvability , 2009, JACM.
[51] Yurii Nesterov,et al. Interior-point polynomial algorithms in convex programming , 1994, Siam studies in applied mathematics.
[52] D. R. Fulkerson,et al. Maximal Flow Through a Network , 1956 .
[53] T. Nakagaki,et al. Intelligence: Maze-solving by an amoeboid organism , 2000, Nature.
[54] Enrico Facca,et al. Towards a Stationary Monge-Kantorovich Dynamics: The Physarum Polycephalum Experience , 2016, SIAM J. Appl. Math..
[55] P. Green. Iteratively reweighted least squares for maximum likelihood estimation , 1984 .
[56] A. E. Eiben,et al. From evolutionary computation to the evolution of things , 2015, Nature.
[57] James Y. Zou,et al. A Slime Mold Solver for Linear Programming Problems , 2012, CiE.
[58] C. Sidney Burrus,et al. Iterative reweighted least-squares design of FIR filters , 1994, IEEE Trans. Signal Process..
[59] Emery N. Brown,et al. Convergence and Stability of Iteratively Re-weighted Least Squares Algorithms , 2014, IEEE Transactions on Signal Processing.
[60] L. Karlovitz,et al. Construction of nearest points in the Lp, p even, and L∞ norms. I , 1970 .
[61] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[62] László A. Végh. A Strongly Polynomial Algorithm for a Class of Minimum-Cost Flow Problems with Separable Convex Objectives , 2016, SIAM J. Comput..
[63] Bhaskar D. Rao,et al. An affine scaling methodology for best basis selection , 1999, IEEE Trans. Signal Process..
[64] Nisheeth K. Vishnoi,et al. IRLS and Slime Mold: Equivalence and Convergence , 2016, ArXiv.
[65] Nicolai Bissantz,et al. Convergence Analysis of Generalized Iteratively Reweighted Least Squares Algorithms on Convex Function Spaces , 2008, SIAM J. Optim..
[66] Umesh Vazirani,et al. Algorithms, games, and evolution , 2014, Proceedings of the National Academy of Sciences.
[67] László A. Végh,et al. A simpler and faster strongly polynomial algorithm for generalized flow maximization , 2017, STOC.