Search Results for "optimization"
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Showing 28 open source projects for "optimization"
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Optimization.jl
Mathematical Optimization in Julia
Optimization.jl provides the easiest way to create an optimization problem and solve it. 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. -
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Optim.jl
Optimization functions for Julia
Univariate and multivariate optimization in Julia. Optim.jl is part of the JuliaNLSolvers family. Optim.jl is a package for univariate and multivariate optimization of functions. -
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Manopt.jl
Optimization on Manifolds in Julia
Optimization Algorithm on Riemannian Manifolds. A framework to implement arbitrary optimization algorithms on Riemannian Manifolds. Library of optimization algorithms on Riemannian manifolds. Easy-to-use interface for (debug) output and recording values during an algorithm run. Several tools to investigate the algorithms, gradients, and optimality criteria. -
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DiffOpt.jl
Differentiating convex optimization programs w.r.t. program parameters
DiffOpt.jl is a package for differentiating convex optimization programs (JuMP.jl or MathOptInterface.jl models) with respect to program parameters. Note that this package does not contain any solver. This package has two major backends, available via the reverse_differentiate! and forward_differentiate! methods, to differentiate models (quadratic or conic) with optimal solutions. Differentiable optimization is a promising field of convex optimization and has many potential applications in game theory, control theory and machine learning. ... -
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The NLopt module for Julia
Package to call the NLopt nonlinear-optimization library from Julia
This module provides a Julia-language interface to the free/open-source NLopt library for nonlinear optimization. NLopt provides a common interface for many different optimization algorithms. -
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InferOpt.jl
Combinatorial optimization layers for machine learning pipelines
InferOpt.jl is a toolbox for using combinatorial optimization algorithms within machine learning pipelines. It allows you to create differentiable layers from optimization oracles that do not have meaningful derivatives. Typical examples include mixed integer linear programs or graph algorithms. -
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EAGO.jl
A development environment for robust and global optimization
EAGO is an open-source development environment for robust and global optimization in Julia. EAGO is a deterministic global optimizer designed to address a wide variety of optimization problems, emphasizing nonlinear programs (NLPs), by propagating McCormick relaxations along the factorable structure of each expression in the NLP. Most operators supported by modern automatic differentiation (AD) packages (e.g., +, sin, cosh) are supported by EAGO and a number of utilities for sanitizing native Julia code and generating relaxations on a wide variety of user-defined functions have been included. ... -
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NLPModels.jl
Data Structures for Optimization Models
This package provides general guidelines to represent non-linear programming (NLP) problems in Julia and a standardized API to evaluate the functions and their derivatives. The main objective is to be able to rely on that API when designing optimization solvers in Julia. -
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LineSearches.jl
Line search methods for optimization and root-finding
Line search methods for optimization and root-finding. This package provides an interface to line search algorithms implemented in Julia. The code was originally written as part of Optim, but has now been separated out to its own package. -
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InfiniteOpt.jl
An intuitive modeling interface for infinite-dimensional optimization
A JuMP extension for expressing and solving infinite-dimensional optimization problems. InfiniteOpt.jl provides a general mathematical abstraction to express and solve infinite-dimensional optimization problems (i.e., problems with decision functions). Such problems stem from areas such as space-time programming and stochastic programming. InfiniteOpt is meant to facilitate intuitive model definition, automatic transcription into solvable models, permit a wide range of user-defined extensions/behavior, and more. -
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SumOfSquares.jl
Sum of Squares Programming for Julia
SumOfSquares.jl is a JuMP extension that, when used in conjunction with MultivariatePolynomial and PolyJuMP, implements a sum of squares reformulation for polynomial optimization. -
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PowerSimulations.jl
Julia for optimization simulation and modeling of PowerSystems
...Provide a flexible modeling framework that can accommodate problems of different complexity and at different time scales. Streamline the construction of large-scale optimization problems to avoid repetition of work when adding/modifying model details. Exploit Julia's capabilities to improve computational performance of large-scale power system quasi-static simulations. The flexible modeling framework is enabled through a modular set of capabilities that enable scalable power system analysis and exploration of new analysis methods. ... -
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ProximalAlgorithms.jl
Proximal algorithms for nonsmooth optimization in Julia
A Julia package for non-smooth optimization algorithms. This package provides algorithms for the minimization of objective functions that include non-smooth terms, such as constraints or non-differentiable penalties. -
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Surrogates.jl
Surrogate modeling and optimization for scientific machine learning
A surrogate model is an approximation method that mimics the behavior of a computationally expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model g which approximates f by training on previous data collected from... -
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Convex.jl
A Julia package for disciplined convex programming
Convex.jl is a Julia package for Disciplined Convex Programming (DCP). Convex.jl makes it easy to describe optimization problems in a natural, mathematical syntax, and to solve those problems using a variety of different (commercial and open-source) solvers. Convex.jl works by transforming the problem—which possibly has nonsmooth, nonlinear constructions like the nuclear norm, the log determinant, and so forth—into a linear optimization problem subject to conic constraints. ... -
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FrankWolfe.jl
Julia implementation for various Frank-Wolfe and Conditional Gradient
This package is a toolbox for Frank-Wolfe and conditional gradient algorithms. Frank-Wolfe algorithms were designed to solve optimization problems where f is a differentiable convex function and C is a convex and compact set. They are especially useful when we know how to optimize a linear function over C in an efficient way. -
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ImplicitDifferentiation.jl
Automatic differentiation of implicit functions
...Reasons can vary depending on your backend, but the most common include calls to external solvers, mutating operations or type restrictions. Those for which automatic differentiation is very slow. A common example is iterative procedures like fixed point equations or optimization algorithms. -
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Functors.jl
Parameterise all the things
Functors.jl provides tools to express a powerful design pattern for dealing with large/ nested structures, as in machine learning and optimization. For large machine learning models, it can be cumbersome or inefficient to work with parameters as one big, flat vector, and structs help manage complexity; but it is also desirable to easily operate over all parameters at once, e.g. for changing precision or applying an optimizer update step. -
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Tulip.jl
Interior-point solver in pure Julia
Tulip is an open-source interior-point solver for linear optimization, written in pure Julia. It implements the homogeneous primal-dual interior-point algorithm with multiple centrality corrections and therefore handles unbounded and infeasible problems. Tulip’s main feature is that its algorithmic framework is disentangled from linear algebra implementations. This allows to seamless integration of specialized routines for structured problems. -
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CoordinateTransformations.jl
A fresh approach to coordinate transformations
...Transformations are designed to be light-weight and efficient enough for, e.g., real-time graphical applications, while support for both explicit and automatic differentiation makes it easy to perform optimization and therefore ideal for computer vision applications such as SLAM (simultaneous localization and mapping). -
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JUDI.jl
Julia Devito inversion
JUDI is a framework for large-scale seismic modeling and inversion and is designed to enable rapid translations of algorithms to fast and efficient code that scales to industry-size 3D problems. The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling operators can also be used as layers in (convolutional) neural networks to implement physics-augmented deep learning algorithms thanks to its implementation of ChainRules's rrule for the linear operators representing the discre wave equation. -
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BinaryBuilder
Binary Dependency Builder for Julia
...Package installation is merely downloading, verifying package integrity and extracting that tarball on the user's computer. No more compiling on user's machines. No more struggling with system package managers. No more needing sudo access to install that little mathematical optimization library. -
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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function... -
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ParallelStencil.jl
Package for writing high-level code for parallel stencil computations
ParallelStencil empowers domain scientists to write architecture-agnostic high-level code for parallel high-performance stencil computations on GPUs and CPUs. Performance similar to CUDA C / HIP can be achieved, which is typically a large improvement over the performance reached when using only CUDA.jl or AMDGPU.jl GPU Array programming. For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding GPU Array... -
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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.
