Search Results for "multi-objective optimization"
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Showing 185 open source projects for "multi-objective 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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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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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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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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Open Source Vizier
Python-based research interface for blackbox
Open Source (OSS) Vizier is a Python-based interface for blackbox optimization and research, based on Google’s original internal Vizier, one of the first hyperparameter tuning services designed to work at scale. Allows a user to setup an OSS Vizier Server, which can host black-box optimization algorithms to serve multiple clients simultaneously in a fault-tolerant manner to tune their objective functions. -
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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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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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DLSSG for SM86
Here is a dlssg for RTX30 Series GPU
DLSSG for SM86 is a Windows proxy that enables NVIDIA DLSS Frame Generation on RTX 30-series Ampere and RTX 20-series Turing GPUs. It works with Direct3D 12 games by loading a proxy DLL beside the rendering executable. The package embeds a matched DLSS-G runtime and custom GPU kernels rather than requiring users to align separate runtime files. It supports multiple generated frames per rendered frame, including higher multipliers when the game’s own plugin permits them. Configurable... -
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Prompt Optimizer
A prompt word optimizer to help write high-quality prompt words
Prompt-Optimizer is a high-impact AI prompt engineering tool designed to help users craft better, more effective prompts for large language models, boosting the quality and relevance of AI responses. It focuses on automating and streamlining the iterative refinement of prompts by analyzing examples, comparing original and optimized text, and guiding users through multi-round improvements that surface clarity, structure, and specificity. With support for different deployment modes including... -
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Pruna AI
Pruna is a model optimization framework built for developers
Pruna is an open-source, self-hostable AI inference engine designed to help teams deploy and manage large language models (LLMs) efficiently across private or hybrid infrastructures. Built with performance and developer ergonomics in mind, Pruna simplifies inference workflows by enabling multi-model orchestration, autoscaling, GPU resource allocation, and compatibility with popular open-source models. It is ideal for companies or teams looking to reduce reliance on external APIs while... -
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Open Gauss
Project-scoped Lean workflow orchestrator from Math, Inc.
...The database organizes data using the relational model, storing structured information in tables composed of rows and columns while supporting standard SQL for querying and management. One of its defining strengths is its optimization for multi-core and distributed environments, allowing it to efficiently process high volumes of concurrent transactions with minimal latency. OpenGauss also incorporates AI-based optimization techniques, such as intelligent query planning, performance prediction, and automated tuning, which help reduce operational complexity and improve efficiency. -
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V2Ray-Agent
Xray, Tuic, hysteria2, sing-box
An all-in-one V2Ray/Xray agent script to automate proxy server deployment on Linux. Supports multiple protocols (VLESS, VMess, Shadowsocks, WireGuard), automatic certificate generation, CDN node management, and multi-VPS subscription configurations—often used for network optimization or censorship circumvention. -
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Robyn
Experimental, AI/ML-powered and open sourced Marketing Mix Modeling
Robyn is an open-source, AI/ML-powered Marketing Mix Modeling (MMM) toolkit developed by Meta Marketing Science under the “facebookexperimental” GitHub umbrella. Its goal is to democratize rigorous MMM: what traditionally required expert statisticians and expensive consulting becomes accessible to any company with data. Robyn takes in historical data (spends on different marketing channels, conversions, or revenue, and optional context or organic-media variables) and uses a combination of... -
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Claude Ads
Comprehensive paid advertising audit & optimization skill
Claude Ads is an AI-powered auditing and optimization tool designed to analyze paid advertising campaigns across multiple platforms using Claude Code. It processes user-provided data such as exports or screenshots and evaluates campaigns using hundreds of predefined checks. The system generates structured reports, identifies inefficiencies, and suggests optimization strategies based on industry benchmarks. It supports platforms like Google Ads, Meta Ads, TikTok, LinkedIn, and more, offering... -
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darwin-skill
Autoresearch-inspired autonomous skill optimization for Claude Code
darwin-skill is an experimental framework designed to automatically improve AI agent “skills” through iterative evaluation and optimization loops inspired by machine learning training processes. Instead of treating prompts or skill definitions as static assets, the system applies a continuous improvement cycle that evaluates performance, proposes changes, tests outcomes, and either retains or reverts modifications. The framework introduces a scoring system across multiple dimensions,... -
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Agent Reinforcement Trainer
Train multi-step agents for real-world tasks using GRPO
Agent Reinforcement Trainer, or ART is an open-source reinforcement learning framework tailored to training large language model agents through experience, making them more reliable and performant on multi-turn, multi-step tasks. Instead of just manually crafting prompts or relying on supervised fine-tuning, ART uses techniques like Group Relative Policy Optimization (GRPO) to let agents learn from environmental feedback and reward signals. The framework is designed to integrate easily with Python applications, abstracting much of the RL infrastructure so developers can train agents without deep RL expertise or heavy infrastructure overhead. ... -
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Halo
Framework built by White Circle for training large language
Halo is an open-source training framework for large language and multimodal models, designed to scale from a single GPU to multi-node clusters. It supports pretraining, supervised fine-tuning, preference optimization, distillation, and fully asynchronous multi-turn reinforcement learning. The framework trains Hugging Face models directly, so checkpoints remain compatible with standard from_pretrained workflows. Distributed execution supports expert, context, tensor, and expert-tensor parallelism alongside FSDP2. ... -
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Axolotl
Go ahead and axolotl questions
Axolotl is a powerful and flexible framework for fine-tuning large language models on custom datasets. Built for researchers and developers, Axolotl simplifies the process of adapting LLMs for specific tasks, including chat, code generation, and instruction following. It supports a wide variety of model architectures and offers out-of-the-box optimization strategies for efficient training. -
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Open Notebook
An Open Source implementation of Notebook LM with more flexibility
...Designed to be self-hosted, it ensures complete data sovereignty by keeping your content local or within your own infrastructure. The platform supports 16+ AI providers—including OpenAI, Anthropic, Ollama, Google, and LM Studio—allowing flexible model choice and cost optimization. Open Notebook enables users to organize and analyze multi-modal content such as PDFs, videos, audio files, web pages, and Office documents. It combines full-text and vector search with context-aware AI chat to deliver insights grounded in your own research materials. With advanced features like multi-speaker podcast generation, customizable content transformations, and a comprehensive REST API, Open Notebook provides a powerful and extensible research environment. -
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Mist Cloud Management Platform
Mist is an open source, multicloud management platform
Mist CE is an open-source multi-cloud management platform, offering unified control and monitoring for hybrid and multi-cloud environments. -
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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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FFmpeg Quality Metrics
Calculate quality metrics with FFmpeg (SSIM, PSNR, VMAF, VIF)
FFmpeg Quality Metrics is a Python-based tool that evaluates video quality by calculating objective metrics using FFmpeg. It supports widely used metrics such as PSNR, SSIM, VIF, MSAD, and VMAF, enabling detailed comparison between reference and distorted video files. The tool outputs both per-frame data and aggregated statistics like averages and standard deviation, making it useful for research, encoding optimization, and benchmarking. -
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LLaMA-Factory
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
LLaMA-Factory is a fine-tuning and training framework for Meta's LLaMA language models. It enables researchers and developers to train and customize LLaMA models efficiently using advanced optimization techniques. -
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DeepSeed
Deep learning optimization library making distributed training easy
...With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of arts, democratizing multi-billion-parameter model training such that many deep learning scientists can explore bigger and better models. Sparse attention of DeepSpeed powers an order-of-magnitude longer input sequence and obtains up to 6x faster execution comparing with dense transformers. -
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FlashAttention
Fast and memory-efficient exact attention
...By improving both forward and backward pass efficiency, it enables training and inference of large language models with longer sequence lengths and higher throughput. The library integrates with PyTorch and supports various attention configurations, including causal masking, multi-query attention, and rotary embeddings.
