Search Results for "algorithms" - Page 2
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Showing 42 open source projects for "algorithms"
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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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InMemoryDatasets.jl
Multithreaded package for working with tabular data in Julia
InMemoryDatasets.jl is a multithreaded package for data manipulation and is designed for Julia 1.6+ (64-bit OS). The core computation engine of the package is a set of customized algorithms developed specifically for columnar tables. -
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DynamicalSystems.jl
Award winning software library for nonlinear dynamics timeseries
...To learn how to use it and see its contents visit the documentation, which you can either find online or build locally by running the docs/make.jl file. DynamicalSystems.jl is part of JuliaDynamics, an organization dedicated to creating high-quality scientific software. All implemented algorithms provide a high-level scientific description of their functionality in their documentation string as well as references to scientific papers. The documentation features hundreds of tutorials and examples ranging from introductory to expert usage. -
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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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WordCloud.jl
Word cloud generator in julia
...Each word appears only once, and its font size is determined solely by the provided weight. Words are never repeated or shrunk artificially to fill empty spaces. It utilizes intelligent strategies and efficient nesting algorithms, implemented entirely in Julia (see Stuffing.jl). As a result, it can easily generate high-resolution word clouds. -
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Optimization.jl
Mathematical Optimization in Julia
...It enables rapid prototyping and experimentation with minimal syntax overhead by providing a uniform interface to >25 optimization libraries, hence 100+ optimization solvers encompassing almost all classes of optimization algorithms such as global, mixed-integer, non-convex, second-order local, constrained, etc. It allows you to choose an Automatic Differentiation (AD) backend by simply passing an argument to indicate the package to use and automatically generates the efficient derivatives of the objective and constraints while giving you the flexibility to switch between different AD engines as per your problem. ... -
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PDFIO.jl
PDF Reader Library for Native Julia.
PDFIO is a native Julia implementation for reading PDF files. It's a 100% Julia implementation of the PDF specification. Other than a few well-established algorithms like flate decode (zlib library) or cryptographic operations (OpenSSL library) almost all of the APIs are written in native Julia. PDF files are in existence for over three decades. Implementations of the PDF writers are not always to the specification or they may even vary significantly from vendor to vendor. Every time, you get a new PDF file there is a possibility that it may not work to the best interpretation of the specification. ... -
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NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers
Fast implementations of root-finding algorithms in Julia that satisfy the SciML common interface. For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation that contains the unreleased features. NonlinearSolve.jl is a unified interface for the nonlinear solving packages of Julia. The package includes its own high-performance nonlinear solvers which include the ability to swap out to fast direct and iterative linear solvers, along with the ability to use sparse automatic differentiation for Jacobian construction and Jacobian-vector products. ... -
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Iterative Solvers
Iterative algorithms for solving linear systems, eigensystems
IterativeSolvers is a Julia package that provides iterative algorithms for solving linear systems, eigensystems, and singular value problems. -
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ProximalOperators.jl
Proximal operators for nonsmooth optimization in Julia
Proximal operators for nonsmooth optimization in Julia. This package can be used to easily implement proximal algorithms for convex and nonconvex optimization problems such as ADMM, the alternating direction method of multipliers. With using ProximalOperators the package exports the prox and prox! methods to evaluate the proximal mapping of several functions. -
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Kinetic.jl
Universal modeling and simulation of fluid mechanics upon ML
Kinetic is a computational fluid dynamics toolbox written in Julia. It aims to furnish efficient modeling and simulation methodologies for fluid dynamics, augmented by the power of machine learning. Based on differentiable programming, mechanical and neural network models are fused and solved in a unified framework. Simultaneous 1-3 dimensional numerical simulations can be performed on CPUs and GPUs. -
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MessyTimeSeries.jl
A Julia implementation of basic tools for time series analysis
A Julia implementation of basic tools for time series analysis compatible with incomplete data. Advanced estimation and validation algorithms are included in MessyTimeSeriesOptim. -
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Indicators.jl
Financial market technical analysis & indicators in Julia
Indicators is a Julia package offering efficient implementations of many technical analysis indicators and algorithms. This work is inspired by the TTR package in R and the Python implementation of TA-Lib, and the ultimate goal is to implement all of the functionality of these offerings (and more) in Julia. This package has been written to be able to interface with both native Julia Array types, as well as the TS time series type from the Temporal package. -
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ReinforcementLearningAnIntroduction.jl
Julia code for the book Reinforcement Learning An Introduction
...One of our main goals is to help users understand the basic concepts of reinforcement learning from an engineer's perspective. Once you have grasped how different components are organized, you're ready to explore a wide variety of modern deep reinforcement learning algorithms in ReinforcementLearningZoo.jl. -
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RigidBodyDynamics.jl
Julia implementation of various rigid body dynamics
RigidBodyDynamics.jl is a rigid body dynamics library in pure Julia. It aims to be user friendly and performant, but also generic in the sense that the algorithms can be called with inputs of any (suitable) scalar types. This means that if fast numeric dynamics evaluations are required, a user can supply Float64 or Float32 inputs. However, if symbolic quantities are desired for analysis purposes, they can be obtained by calling the algorithms with e.g. SymPy.Sym inputs. If gradients are required, e.g. the ForwardDiff.Dual type, which implements forward-mode automatic differentiation, can be used. -
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ArrayFire.jl
Julia wrapper for the ArrayFire library
ArrayFire is a library for GPU and accelerated computing. ArrayFire.jl wraps the ArrayFire library for Julia, and provides a Julia interface. Install ArrayFire library: either download a binary from the official site, or you can build from source. -
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HMMBase.jl
Hidden Markov Models for Julia
HMMBase is not maintained anymore. It will keep being available as a Julia package but we encourage existing and new users to migrate to HiddenMarkovModels.jl which offers a similar interface. For more information see HiddenMarkovModels.jl: when did HMMs get so fast?. HMMBase provides a lightweight and efficient abstraction for hidden Markov models in Julia. Most HMMs libraries only support discrete (e.g. categorical) or Normal distributions. In contrast HMMBase builds upon Distributions.jl...
