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Keyword

first-order methods

Description: First-order methods are optimization algorithms that use objective-function values and first derivatives to iteratively seek an optimum.
OpenAlex ID: keywords/first-order-methods
ADADELTA: An Adaptive Learning Rate Method
2012 · Zeiler, Matthew D. · arXiv (Cornell University) · 5,321
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A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
2010 · Antonin Chambolle, Thomas Pock · Journal of Mathematical Imaging and Vision · 4,322
First-Order Methods in Optimization
2017 · Amir Beck · Society for Industrial and Applied Mathematics eBooks · 1,564
First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method
1992 · Roberto Battiti · Neural Computation · 1,193
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
2009 · Stephen Becker, Jérôme Bobin, et al. · 897
Optimization for Machine Learning
2011 · Sra, S., Nowozin, S., et al. · The MIT Press eBooks · 883
Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding
2016 · Brendan O’Donoghue, Eric Chu, et al. · Journal of Optimization Theory and Applications · 662
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Best subset selection via a modern optimization lens
2016 · Dimitris Bertsimas, Angela King, et al. · The Annals of Statistics · 655
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Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming
2011 · A. Belloni, V. Chernozhukov, et al. · Biometrika · 635
Templates for convex cone problems with applications to sparse signal recovery
2011 · Stephen R. Becker, Emmanuel J. Candès, et al. · Mathematical Programming Computation · 608
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Scalable tensor factorizations for incomplete data
2010 · Evrim Acar, Daniel M. Dunlavy, et al. · Chemometrics and Intelligent Laboratory Systems · 602
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An introduction to continuous optimization for imaging
2016 · Antonin Chambolle, Thomas Pock · Acta Numerica · 545
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Gradient Descent Provably Optimizes Over-parameterized Neural Networks
2018 · Du, Simon S., Zhai, Xiyu, et al. · arXiv (Cornell University) · 490
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Accelerated gradient methods for nonconvex nonlinear and stochastic programming
2015 · Saeed Ghadimi, Guanghui Lan · Mathematical Programming · 487
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Hankel Matrix Rank Minimization with Applications to System Identification and Realization
2013 · Maryam Fazel, Ting Kei Pong, et al. · SIAM Journal on Matrix Analysis and Applications · 486
Fixed-Point Algorithms for Inverse Problems in Science and Engineering
2011 · Bauschke, Heinz H, Burachik, Regina S, et al. · Springer optimization and its applications · 460
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An Inertial Forward-Backward Algorithm for Monotone Inclusions
2014 · Dirk A. Lorenz, Thomas Pock · Journal of Mathematical Imaging and Vision · 451
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Empirical evaluation of the improved Rprop learning algorithms
2003 · Christian Igel, Michael Hüsken · Neurocomputing · 433
On the ergodic convergence rates of a first-order primal–dual algorithm
2015 · Antonin Chambolle, Thomas Pock · Mathematical Programming · 413
Editorial Policy Statement on the Control of Numerical Accuracy
1986 · Patrick J. Roache, Kirti N. Ghia, et al. · Journal of Fluids Engineering · 359
First-Order Methods for Sparse Covariance Selection
2008 · Alexandre d'Aspremont, Onureena Banerjee, et al. · SIAM Journal on Matrix Analysis and Applications · 343
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A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications
2016 · Heinz H. Bauschke, Jérôme Bolte, et al. · Mathematics of Operations Research · 326
Compressed Sensing With Quantized Measurements
2009 · A. Zymnis, S. Boyd, et al. · IEEE Signal Processing Letters · 316
Exploring the Landscape of Spatial Robustness
2017 · Engstrom, Logan, Tran, Brandon, et al. · arXiv (Cornell University) · 300
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Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics
2014 · Volkan Cevher, Stephen Becker, et al. · IEEE Signal Processing Magazine · 300
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Works count: 2,560
Citation count: 50,440
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