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A Flexible Stochastic Multi-Agent ADMM Method for Large-Scale Distributed Optimization
IEEE Access, 2022 While applying stochastic alternating direction method of multiplier (ADMM) methods has become enormously potential in distributed applications, improving the algorithmic flexibility can bring huge benefits.
Lin Wu, Yongbin Wang, Tuo Shi
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Variance Reduction with Sparse Gradients
CoRR, 2020 Variance reduction methods such as SVRG and SpiderBoost use a mixture of large and small batch gradients to reduce the variance of stochastic gradients. Compared to SGD, these methods require at least double the number of operations per update to model parameters.
Melih Elibol +2 more
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Variance reduction methods [PDF]
Proceedings of the 18th conference on Winter simulation - WSC '86, 1986 A computer simulation model is unusual in that the random error is under the total control of the experimenter. Variance reduction methods aim to take advantage of this to improve experimental accuracy. The fundamental ideas behind the most important of these methods will be described and illustrated with simple examples.
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A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models
Risks, 2020 We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface.
Christa Cuchiero +2 more
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Dimensionality Reduction Mappings [PDF]
, 2011 A wealth of powerful dimensionality reduction methods has been established which can be used for data visualization and preprocessing. These are accompanied by formal evaluation schemes, which allow a quantitative evaluation along general principles and ...
Hammer, Barbara ; https://orcid.org/ +12 more
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Semi-Stochastic Gradient Descent Methods
Frontiers in Applied Mathematics and Statistics, 2017 In this paper we study the problem of minimizing the average of a large number of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full gradient
Jakub Konečný, Peter Richtárik
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Mathematics, 2023 Monte Carlo simulation is performed with uniformly distributed U(0,1) pseudo-random numbers. Because the numbers are generated from a mathematical formula, they will contain some serial correlation, even if very small.
Dennis Ridley, Pierre Ngnepieba
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Integrated Variance Reduction Strategies [PDF]
Proceedings of 1993 Winter Simulation Conference - (WSC '93), 1993 In this paper we develop strategies for integrating certain well-known variance reduction techniques to estimate a mean response in a finite-horizon simulation experiment. Our building blocks are the techniques of conditional expectation, correlation induction, and control variates.
Athanassios N. Avramidis +1 more
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Stochastic Momentum Method With Double Acceleration for Regularized Empirical Risk Minimization
IEEE Access, 2019 Momentum acceleration technique is famously known for building gradient-based algorithms with fast convergence in large-scale optimization. Recently, Nesterov 's momentum and Katyusha momentum have significantly improved the convergence for stochastic ...
Zhijian Luo, Siyu Chen, Yuntao Qian
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Variance Reduction of Sequential Monte Carlo Approach for GNSS Phase Bias Estimation
Mathematics, 2020 Global navigation satellite systems (GNSS) are an important tool for positioning, navigation, and timing (PNT) services. The fast and high-precision GNSS data processing relies on reliable integer ambiguity fixing, whose performance depends on phase bias
Yumiao Tian, Maorong Ge, Frank Neitzel
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