In this chapter we propose a dual decomposition method based on inexact dual gradient information and constraint tightening for solving distributed model predictive control (MPC) problems for network systems with state-input constraints. The coupling constraints are tightened and moved in the cost using...
CHAKRABORTY N, SYCARA K. Provably-good distributed algorithm for constrained multi-robot task assignme...
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. - microsoft/LightGBM
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked ...
We propose a smoothing accelerated proximal gradient (SAPG) method with fast convergence rate for finding a minimizer of a decomposable nonsmooth convex fu
In contrast, variational methods can be parallelized at the sample level in each iteration—for example, gradient calculation in ADVI, normalizing flows and SVGD can be fully parallelized. In addition variational methods can be applied to large datasets by using stochastic optimization (Kubrusly & ...
Methods Gradient fitting based algorithm. Generally, the emitter's PSF profile (I) in the astigmatism-based microscopy can be approximated by a 2D elliptical Gaussian function, which can be expressed by the following equation:5 I (m, n) = N 2πwx wy exp −...
18. Nonlinear dimensionality reduction methods have also become the standard approach for single-cell data visualization. For example,t-distributed stochastic neighbor embedding19and uniform manifold approximation and projection (UMAP)20are two widely used algorithms for this purpose, despite recent concerns...
This results in methods such as stochastic gradient descent and nonlinear conjugate gradients taking tiny steps towards a minimum. The optimization proceeds so slowly that in many cases the researcher stops training after a predetermined number of iterations, so that the weights obtained may be far ...
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. - catboost/catboost