Stochastic Proximal Gradient Consensus Over Random Networks
We consider solving a convex optimization problem with possibly stochastic gradient, and over a randomly time-varying multiagent network. Each agent has access to some local objective function, and it only has unbiased estimates of the gradients of the smooth component. We develop a dynamic stochast...
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| Veröffentlicht in: | IEEE transactions on signal processing Jg. 65; H. 11; S. 2933 - 2948 |
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01.06.2017
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| Abstract | We consider solving a convex optimization problem with possibly stochastic gradient, and over a randomly time-varying multiagent network. Each agent has access to some local objective function, and it only has unbiased estimates of the gradients of the smooth component. We develop a dynamic stochastic proximal-gradient consensus algorithm, with the following key features: (1) it works for both the static and certain randomly time-varying networks; (2) it allows the agents to utilize either the exact or stochastic gradient information; (3) it is convergent with provable rate. In particular, the proposed algorithm converges to a global optimal solution, with a rate of O(1/r) [resp. O(1/√r)] when the exact (resp. stochastic) gradient is available, where r is the iteration counter. Interestingly, the developed algorithm establishes a close connection among a number of (seemingly unrelated) distributed algorithms, such as the EXTRA, the PG-EXTRA, the IC/IDC-ADMM, the DLM, and the classical distributed subgradient method. |
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| AbstractList | We consider solving a convex optimization problem with possibly stochastic gradient, and over a randomly time-varying multiagent network. Each agent has access to some local objective function, and it only has unbiased estimates of the gradients of the smooth component. We develop a dynamic stochastic proximal-gradient consensus algorithm, with the following key features: (1) it works for both the static and certain randomly time-varying networks; (2) it allows the agents to utilize either the exact or stochastic gradient information; (3) it is convergent with provable rate. In particular, the proposed algorithm converges to a global optimal solution, with a rate of O(1/r) [resp. O(1/√r)] when the exact (resp. stochastic) gradient is available, where r is the iteration counter. Interestingly, the developed algorithm establishes a close connection among a number of (seemingly unrelated) distributed algorithms, such as the EXTRA, the PG-EXTRA, the IC/IDC-ADMM, the DLM, and the classical distributed subgradient method. |
| Author | Tsung-Hui Chang Mingyi Hong |
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| Cites_doi | 10.1109/TSP.2016.2544743 10.1137/140990309 10.1137/080716542 10.1109/TAC.2014.2364096 10.1109/TAC.2017.2713046 10.1109/TAC.2010.2041686 10.1109/TSP.2007.906734 10.1109/TSP.2014.2367458 10.1137/14096668X 10.1109/TAC.2008.2009515 10.1109/TSP.2011.2162831 10.1109/TAC.2011.2161027 10.1016/j.jpdc.2006.08.010 10.1109/JSTSP.2011.2114324 10.1109/TAC.2014.2363299 10.1109/ICASSP.2015.7178514 10.1109/TSP.2015.2436358 10.1109/TSP.2008.919636 10.1109/TSIPN.2016.2613678 10.1109/TSP.2013.2254478 10.1561/2200000016 10.1137/140971233 10.1109/ALLERTON.2014.7028466 10.1109/TAC.2014.2298712 10.1109/ICASSP.2016.7472584 10.1109/JSTSP.2011.2118740 10.1109/TWC.2010.06.090890 10.1007/s10957-010-9737-7 10.1109/TSP.2010.2055862 10.1109/MLSP.2014.6958866 10.1017/CBO9780511804458.011 10.1109/GlobalSIP.2013.6736937 10.1109/CDC.2008.4738860 10.1007/978-1-4419-8853-9 10.1137/14095697X 10.1007/978-3-662-12613-4 10.1109/ICASSP.2014.6855109 10.1109/TSP.2014.2304432 |
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| References | ref13 ref12 ref15 ref14 ref11 ref10 ref17 ref19 ref18 tsitsiklis (ref2) 1984 hong (ref46) 2015 hajinezhad (ref51) 2016 ref48 ref47 ref42 ref44 ref8 ref7 ref4 ref5 nesterov (ref52) 2004 ref40 mokhtari (ref20) 2016; 17 ref35 ref37 ram (ref38) 2010; 147 ref36 ref31 ref30 ref33 ref32 ref1 le roux (ref49) 0 ref39 bertsekas (ref22) 1999 chen (ref16) 2012 gao (ref41) 2014 ref24 shalev-shwartz (ref6) 2013; 14 ref23 ref26 ref25 ref21 zhong (ref43) 2014 ref28 ouyang (ref45) 2013 ref27 ref29 xiao (ref3) 2007; 67 pesquet (ref34) 2015; 16 johnson (ref50) 2013 giannakis (ref9) 2015 |
| References_xml | – ident: ref40 doi: 10.1109/TSP.2016.2544743 – ident: ref33 doi: 10.1137/140990309 – ident: ref48 doi: 10.1137/080716542 – ident: ref19 doi: 10.1109/TAC.2014.2364096 – start-page: 80 year: 2013 ident: ref45 article-title: Stochastic alternating direction method of multipliers publication-title: Proc Int Conf Mach Learn – ident: ref32 doi: 10.1109/TAC.2017.2713046 – ident: ref11 doi: 10.1109/TAC.2010.2041686 – ident: ref7 doi: 10.1109/TSP.2007.906734 – volume: 14 start-page: 567 year: 2013 ident: ref6 article-title: Proximal stochastic dual coordinate ascent methods for regularized loss minimization publication-title: J Mach Learn Res – year: 2015 ident: ref46 article-title: Stochastic proximal gradient consensus over random networks – start-page: 2672 year: 0 ident: ref49 article-title: A stochastic gradient method with an exponential convergence rate for strongly-convex optimization with finite training sets publication-title: Proc Neural Inf Process Syst – ident: ref30 doi: 10.1109/TSP.2014.2367458 – ident: ref14 doi: 10.1137/14096668X – year: 1999 ident: ref22 publication-title: Parallel and Distributed Computation Numerical Methods – ident: ref10 doi: 10.1109/TAC.2008.2009515 – volume: 16 start-page: 2453 year: 2015 ident: ref34 article-title: A class of randomized primal-dual algorithms for distributed optimization publication-title: J Nonlinear Convex Anal – ident: ref29 doi: 10.1109/TSP.2011.2162831 – volume: 17 start-page: 2165 year: 2016 ident: ref20 article-title: DSA: Decentralized double stochastic averaging gradient algorithm publication-title: J Mach Learn Res – ident: ref18 doi: 10.1109/TAC.2011.2161027 – volume: 67 start-page: 33 year: 2007 ident: ref3 article-title: Distributed average consensus with least-mean-square deviation publication-title: J Parallel Distrib Comput doi: 10.1016/j.jpdc.2006.08.010 – start-page: 78 year: 2014 ident: ref43 article-title: Fast stochastic alternating direction method of multipliers publication-title: Proc 31st Int Conf Mach Learn – ident: ref4 doi: 10.1109/JSTSP.2011.2114324 – ident: ref37 doi: 10.1109/TAC.2014.2363299 – ident: ref15 doi: 10.1109/ICASSP.2015.7178514 – ident: ref31 doi: 10.1109/TSP.2015.2436358 – ident: ref47 doi: 10.1109/TSP.2008.919636 – ident: ref21 doi: 10.1109/TSIPN.2016.2613678 – ident: ref28 doi: 10.1109/TSP.2013.2254478 – ident: ref23 doi: 10.1561/2200000016 – ident: ref36 doi: 10.1137/140971233 – ident: ref27 doi: 10.1109/ALLERTON.2014.7028466 – ident: ref17 doi: 10.1109/TAC.2014.2298712 – start-page: 3215 year: 2013 ident: ref50 article-title: Accelerating stochastic gradient descent using predictive variance reduction publication-title: Proc 26th Int Conf Neural Inf Process – ident: ref1 doi: 10.1109/ICASSP.2016.7472584 – ident: ref13 doi: 10.1109/JSTSP.2011.2118740 – ident: ref8 doi: 10.1109/TWC.2010.06.090890 – year: 2015 ident: ref9 article-title: Proximal splitting methods in signal processing publication-title: Splitting Methods in Communication and Imaging – volume: 147 start-page: 516 year: 2010 ident: ref38 article-title: Distributed stochastic subgradient projection algorithms for convex optimization publication-title: J Optim Theory Appl doi: 10.1007/s10957-010-9737-7 – year: 1984 ident: ref2 article-title: Problems in decentralized decision making and computation – year: 2012 ident: ref16 article-title: Fast distributed first-order methods – start-page: 3215 year: 2016 ident: ref51 article-title: NESTT: A nonconvex primal-dual splitting method for distributed and stochastic optimization publication-title: Proc Neural Inf Process Syst – ident: ref5 doi: 10.1109/TSP.2010.2055862 – ident: ref35 doi: 10.1109/MLSP.2014.6958866 – ident: ref39 doi: 10.1017/CBO9780511804458.011 – ident: ref25 doi: 10.1109/GlobalSIP.2013.6736937 – ident: ref12 doi: 10.1109/CDC.2008.4738860 – year: 2004 ident: ref52 publication-title: Introductory Lectures on Convex Optimization A Basic Course doi: 10.1007/978-1-4419-8853-9 – ident: ref42 doi: 10.1137/14095697X – ident: ref24 doi: 10.1007/978-3-662-12613-4 – ident: ref44 doi: 10.1109/ICASSP.2014.6855109 – ident: ref26 doi: 10.1109/TSP.2014.2304432 – year: 2014 ident: ref41 article-title: On the information-adaptive variants of the ADMM: An iteration complexity perspective |
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| SubjectTerms | ADMM Algorithm design and analysis Convergence Convex functions Distributed optimization fast algorithms Heuristic algorithms Linear programming Optimization rate analysis Signal processing algorithms |
| Title | Stochastic Proximal Gradient Consensus Over Random Networks |
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