Search Results for "decomposition"
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Showing 9 open source projects for "decomposition"
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Coluna.jl
Branch-and-Price-and-Cut in Julia
...You write an original MIP that models your problem using the JuMP modeling language and our specific extension BlockDecomposition offers a syntax to specify the problem decomposition. Then, Coluna reformulates the original MIP and optimizes the reformulation using the algorithms you choose. Coluna aims to be very modular and tweakable so that you can define the behavior of your customized branch-and-price-and-cut algorithm. -
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Circuitscape.jl
Algorithms from circuit theory to predict connectivity
Circuitscape is an open-source program that uses circuit theory to model connectivity in heterogeneous landscapes. Its most common applications include modeling the movement and gene flow of plants and animals, as well as identifying areas important for connectivity conservation. The new Circuitscape is built entirely in the Julia language, a new programming language for technical computing. Julia is built from the ground up to be fast. As such, this offers a number of advantages over the... -
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mapcn
Beautiful map components, 100% Free, Zero config, one command setup
...The core idea is to enable agents to generalize learned behavior by representing continuous control policies in a compact, interpretable form that preserves smoothness and controllability. The project includes implementations that experiment with policy encoding, action decomposition techniques, and sample efficiency analysis in classic reinforcement learning environments. By organizing action outputs into meaningful, lower-dimensional manifolds, MapCN attempts to improve both learning convergence and transfer performance when compared to unguided continuous control baselines. -
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forecast
Forecasting Functions for Time Series and Linear Models
The forecast package is a comprehensive R package for time series analysis and forecasting. It provides functions for building, assessing, and using univariate forecasting models (e.g. ARIMA, exponential smoothing, etc.), tools for automatic model selection, diagnostics, plotting, forecasting future values, etc. It's widely used in statistics, economics, business forecasting, environmental science, etc. Exponential smoothing state space models (ETS) including seasonal components. Residual... -
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GTB: Graphics Toolbox
C++ libraries and apps for computer graphics and data visualization
The Graphics Toolbox (GTB) is a collection of C++ libraries and apps for computer graphics and data visualization. Wagner Correa initially created GTB as part of his Ph.D. research at Princeton University in collaboration with Professor Claudio Silva and Dr. James Klosowski. Several other researchers later contributed to GTB (see the AUTHORS file). -
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LaNet-vi provides images of large scale networks on a two-dimensional layout. The algorithm is based on the k-core and k-dense (m-core) decomposition. For more details see http://lanet-vi.fi.uba.ar.
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Universal Java Matrix Package
sparse and dense matrix, linear algebra, visualization, big data
The Universal Java Matrix Package (UJMP) is an open source Java library which provides sparse and dense matrix classes, as well as a large number of calculations for linear algebra such as matrix multiplication or matrix inverse. Operations such as mean, correlation, standard deviation, replacement of missing values or the calculation of mutual information are supported, too. The Universal Java Matrix Package provides various visualization methods, import and export filters for a large... -
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Qi - Quantitative Imaging (PIV and more)
A collection of MATLAB tools for PIV, PTV, particle sizing, and more.
...Our Particle Image Velocimetry (PIV) tool, prana, implements a Robust Phase Correlation kernel for PIV analysis, and now includes Particle Tracking Velocimetry and sizing tools. Tools for pressure calculation, proper orthogonal decomposition (POD), and 3d imaging are under development. -
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...It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small-world, scale-free, exponential random graphs, Fiedler random fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition.
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