Search Results for "optimization"
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Showing 369 open source projects for "optimization"
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Theseus
A library for differentiable nonlinear optimization
...Theseus bridges the gap between classical optimization and deep learning, enabling hybrid systems that learn components. -
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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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Nevergrad
A Python toolbox for performing gradient-free optimization
Nevergrad is a Python library for derivative-free optimization, offering robust implementations of many algorithms suited for black-box functions (i.e. functions where gradients are unavailable or unreliable). It targets hyperparameter search, architecture search, control problems, and experimental tuning—domains in which gradient-based methods may fail or be inapplicable. The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and then experiment with multiple strategies—evolutionary algorithms, Bayesian optimization, bandit methods, genetic algorithms, etc. ... -
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Grey Wolf Optimizer for Path Planning
Grey Wolf Optimizer (GWO) path planning/trajectory
...It allows simulation of both two-dimensional and three-dimensional UAV trajectory planning depending on parameter setups. The tool provides built-in functions to configure different UAV environments and supports multiple optimization objectives. It includes progress visualization to help monitor the optimization process during simulations. Users can adjust objective function weights and experiment with multiple heuristic search strategies to explore optimal solutions. This project demonstrates applications in multi-agent and multi-UAV cooperative path planning, making it useful for research and educational purposes in the field of intelligent optimization and robotics. -
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Optuna
A hyperparameter optimization framework
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters. Optuna Dashboard is a real-time web dashboard for Optuna. -
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BayesianOptimization
A Python implementation of global optimization with gaussian processes
BayesianOptimization is a Python library that helps find the maximum (or minimum) of expensive or unknown objective functions using Bayesian optimization. This technique is especially useful for hyperparameter tuning in machine learning, where evaluating the objective function is costly. The library provides an easy-to-use API for defining bounds and optimizing over parameter spaces using probabilistic models like Gaussian Processes. -
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Soot
Soot - A Java optimization framework
Soot is a Java optimization framework. It provides four intermediate representations for analyzing and transforming Java bytecode. Baf: a streamlined representation of bytecode which is simple to manipulate. Jimple: a typed 3-address intermediate representation suitable for optimization. Shimple: an SSA variation of Jimple. Grimp: an aggregated version of Jimple suitable for decompilation and code inspection. -
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Fabric API
Essential hooks for modding with Fabric
...Adding events, hooks and APIs to improve interoperability between mods. Essential features such as registry synchronization and adding information to crash reports. An advanced rendering API designed for compatibility with optimization mods and graphics overhaul mods. Also check out Fabric Loader, the (mostly) version-independent mod loader that powers Fabric. Fabric API is a mod like any other Fabric mod which requires Fabric Loader to be installed. -
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CasADi
CasADi is a symbolic framework for numeric optimization
CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT, etc. It can be used in C++, Python, or Matlab/Octave. CasADi's backbone is a symbolic framework implementing forward and reverse modes of AD on expression graphs to construct gradients, large-and-sparse Jacobians, and Hessians. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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OptScale
FinOps and MLOps platform to run ML/AI and regular cloud workloads
...OptScale allows ML teams to multiply the number of ML/AI experiments running in parallel while efficiently managing and minimizing costs associated with cloud and infrastructure resources. OptScale MLOps capabilities include ML model leaderboards, performance bottleneck identification and optimization, bulk run of ML/AI experiments, experiment tracking, and more. The solution enables ML/AI engineers to run automated experiments based on datasets and hyperparameter conditions within the defined infrastructure budget. Certified FinOps solution with the best cloud cost optimization engine, providing rightsizing recommendations, Reserved Instances/Savings Plans, and dozens of other optimization scenarios. ... -
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PlatEMO
Evolutionary multi-objective optimization platform
Evolutionary multi-objective optimization platform. PlatEMO consists of a number of MATLAB functions without using any other libraries. Any machines able to run MATLAB can use PlatEMO regardless of the operating system. PlatEMO includes more than ninety existing popular MOEAs, including genetic algorithm, differential evolution, particle swarm optimization, memetic algorithm, estimation of distribution algorithm, and surrogate model-based algorithm. -
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RLax
Library of JAX-based building blocks for reinforcement learning agents
...RLax is fully JIT-compilable with JAX, enabling high-performance execution across CPU, GPU, and TPU backends. The library implements tools for Bellman equations, return distributions, general value functions, and policy optimization in both continuous and discrete action spaces. It integrates seamlessly with DeepMind’s Haiku (for neural network definition) and Optax (for optimization), making it a key component in modular RL pipelines. -
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NFH Self-Improvement Loop
Minimal adversarial framework for AI agent self-modification
...The project emphasizes iterative learning, allowing systems to evolve over time through repeated evaluation and adjustment. It can be applied to areas such as content generation, decision-making, and personal productivity systems. The framework encourages structured reflection and optimization, ensuring that each iteration builds upon previous results. It is particularly useful for experimenting with autonomous improvement processes in AI workflows. Overall, it provides a practical model for implementing continuous optimization in AI-driven systems. -
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Binaryen
Compiler infrastructure and toolchain library for WebAssembly
...It accepts input in WebAssembly-like form but also accepts a general control flow graph for compilers that prefer that. Binaryen's internal IR uses compact data structures and is designed for completely parallel codegen and optimization, using all available CPU cores. Binaryen's IR also compiles down to WebAssembly extremely easily and quickly because it is essentially a subset of WebAssembly. Binaryen's optimizer has many passes (see an overview later down) that can improve code size and speed. These optimizations aim to make Binaryen powerful enough to be used as a compiler backend by itself. -
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claude-code-best-practice
Practice made claude perfect
...It also explores operational concerns such as permissions management, sandboxing, debugging workflows, and context optimization. By combining conceptual guidance with concrete examples and configuration patterns, the project helps teams move from experimental AI usage toward more production-ready agent orchestration. -
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SciPy
SciPy library main repository
This is the main repository for the SciPy library, one of the core packages that make up the SciPy stack. SciPy is an open source software used in the fields of mathematics, science, and engineering, with modules for statistics, optimization, integration, linear algebra, signal and image processing, and many more. The SciPy library contains many of the user-friendly and efficient numerical routines, including those for numerical integration, interpolation, and optimization. SciPy is built to work with NumPy, a software that provides convenient and fast N-dimensional array manipulation. ... -
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NNCF
Neural Network Compression Framework for enhanced OpenVINO
NNCF (Neural Network Compression Framework) is an optimization toolkit for deep learning models, designed to apply quantization, pruning, and other techniques to improve inference efficiency. -
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Apache SkyWalking
Application Performance Monitoring System
...Apache Skywalking supports the collection of telemetry data from a number of different sources and in different formats, such as Java, .NET Core, PHP, LUA agent and more. It also offers a solid core features set, which includes root cause analysis, service topology map analysis, performance optimization, and many more. -
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EasyR1
An Efficient, Scalable, Multi-Modality RL Training Framework
...The framework is also organized to help you compare training strategies (e.g., pure SFT vs. preference optimization) so you can see what actually moves metrics in math, code, and multi-step reasoning. For teams exploring open reasoning models, EasyR1 provides an opinionated yet flexible path from dataset to deployable checkpoints. -
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Anomalib
An anomaly detection library comprising state-of-the-art algorithms
...Its design supports unsupervised or semi-supervised paradigms, making it especially powerful for scenarios where only “normal” data is readily available and defects must be detected without exhaustive labeling. Combined with its CLI and integration with optimization tools like OpenVINO, it’s suitable for both research and edge deployment tasks. -
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Tun2Socks
tun2socks , powered by gVisor TCP/IP stack
...Proxy Protocols: HTTP/Socks4/Socks5/Shadowsocks with authentication support for remote connections. Run Everywhere. Linux/macOS/Windows/FreeBSD/OpenBSD multi-platform support with specific optimization. Gateway Mode: Act as a layer three gateway to handle network traffic from other devices in the same network. Full IPv6 Support: All functions work in IPv6, tunnel IPv4 connections through IPv6 proxy and vice versa. Network Stack: Powered by user-space TCP/IP stack from Google container application kernel gVisor. -
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TBOX
A glib-like multi-platform c library
TBOX is a glib-like cross-platform C library that is simple to use yet powerful in nature. The project focuses on making C development easier and provides many modules (.e.g stream, coroutine, regex, container, algorithm ...), so that any developer can quickly pick it up and enjoy the productivity boost when developing in C language. It supports the following platforms: Windows, Macosx, Linux, Android, iOS, BSD and etc. Supports file, data, http and socket source. Supports the stream filter... -
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Manifolds.jl
Manifolds.jl provides a library of manifolds
Package Manifolds.jl aims to provide both a unified interface to define and use manifolds as well as a library of manifolds to use for your projects. This package is mostly stable, see #438 for planned upcoming changes. The implemented manifolds are accompanied by their mathematical formulae. The manifolds are implemented using the interface for manifolds given in ManifoldsBase.jl. You can use that interface to implement your own software on manifolds, such that all manifolds based on that... -
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RuboCop Performance
An extension of RuboCop focused on code performance checks
Performance optimization analysis for your projects, as an extension to RuboCop. You need to tell RuboCop to load the Performance extension. Now you can run rubocop and it will automatically load the RuboCop Performance cops together with the standard cops. You need to tell RuboCop to load the Performance extension. Now you can run rubocop and it will automatically load the RuboCop Performance cops together with the standard cops. -
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Booster
Optimizer for mobile applications
Booster is an easy-to-use, lightweight, powerful and extensible quality optimization toolkit designed specially for mobile applications. The primary goal is to solve quality problems with the increase of APP complexity, such as performance, stability, and package size, etc. Booster provides a collection of modules for performance detection, multithreading optimization, resources index inline, redundant resources reduction, resources compression, system bug fixing, etc. ...
