Search Results for "optimizer"
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Showing 61 open source projects for "optimizer"
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Grey Wolf Optimizer for Path Planning
Grey Wolf Optimizer (GWO) path planning/trajectory
The Grey Wolf Optimizer for Path Planning is a MATLAB-based implementation of the Grey Wolf Optimizer (GWO) algorithm designed for UAV path and trajectory planning. 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. -
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ProGuard
ProGuard, Java optimizer and obfuscator
ProGuard is a free shrinker, optimizer, obfuscator, and preverifier for Java bytecode. It detects and removes unused classes, fields, methods, and attributes. It optimizes bytecode and removes unused instructions. It renames the remaining classes, fields, and methods using short meaningless names. ProGuard can be run as a task in Gradle. Before you can use the proguard task, you have to make sure Gradle can find it in its class path at build time. -
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ReDex
A bytecode optimizer for Android apps
ReDex is an Android bytecode (dex) optimizer originally developed at Facebook. It provides a framework for reading, writing, and analyzing .dex files, and a set of optimization passes that use this framework to improve the bytecode. An APK optimized by ReDex should be smaller and faster than its source. Redex provides a framework for reading, writing, and analyzing .dex files, and a set of optimization passes that use this framework to improve the bytecode. -
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CCC
Claude Opus 4.6 wrote a dependency-free C compiler in Rust
Claude’s C Compiler is a remarkable experiment in automated software generation: a full C compiler written from scratch in Rust entirely by an AI model (Claude Opus 4.6) with minimal human intervention, capable of targeting multiple architectures like x86-64, i686, ARM, and RISC-V. The project implements every part of a traditional compiler — the lexer, parser, intermediate representation, optimizer, code generator, assembler, linker, and debug info emitter — without relying on any external toolchain, producing standalone executables in the ELF format. While still early and not fully validated for correctness, it demonstrates the frontier of what sophisticated AI can build in complex, systems-level software domains, including the potential to compile real operating systems and large software projects. -
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Polars
Dataframes powered by a multithreaded, vectorized query engine
Polars is a high-performance, multi-language DataFrame library built in Rust using Apache Arrow. It delivers blazing-fast, vectorized, and parallel data manipulation with both eager and lazy execution, making it an excellent tool for data processing in Python, Rust, Node.js, R, and SQL contexts. -
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rtabmap
RTAB-Map library and standalone application
...The loop closure detector uses a bag-of-words approach to determine how likely a new image comes from a previous location or a new location. When a loop closure hypothesis is accepted, a new constraint is added to the map’s graph, then a graph optimizer minimizes the errors in the map. A memory management approach is used to limit the number of locations used for loop closure detection and graph optimization so that real-time constraints on large-scale environments are always respected. RTAB-Map can be used alone with a handheld Kinect, a stereo camera or a 3D lidar for 6DoF mapping, or on a robot equipped with a laser rangefinder for 3DoF mapping. -
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Optimizer_sovkov
Constructing and optimizing general mathematical and physical models
We present the package Optimizer, aimed at constructing and optimizing general mathematical models of phenomena of versatile nature. It is written in the Matlab algorithmic language and is executed in the Matlab environment with partial functionality in Octave. The convenient visual interface and the detailed manuals are provided. The main benefit of the package is its capability to construct models of any level of complexity in a block-by-block manner. -
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The LLVM Compiler Infrastructure
Collection of modular and reusable compiler and toolchain technologies
...Code in the LLVM project is licensed under the "Apache 2.0 License with LLVM exceptions". The LLVM Core libraries provide a modern source- and target-independent optimizer, along with code generation support for many popular CPUs (as well as some less common ones!). -
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MIVisionX
Set of comprehensive computer vision & machine intelligence libraries
...AMD MIVisionX delivers highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized computer vision and machine learning inference workloads on a wide range of computer hardware, including small embedded x86 CPUs, APUs, discrete GPUs, and heterogeneous servers. AMD OpenVX is a highly optimized open-source implementation of the Khronos OpenVX™ 1.3 computer vision specification. ... -
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TensorRT
C++ library for high performance inference on NVIDIA GPUs
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers, embedded, or automotive product platforms. ... -
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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. Using booster, the... -
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Binaryen
Compiler infrastructure and toolchain library for WebAssembly
...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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StructuralEquationModels.jl
A fast and flexible Structural Equation Modelling Framework
This is a package for Structural Equation Modeling in development. It is written for extensibility, that is, you can easily define your own objective functions and other parts of the model. At the same time, it is (very) fast. We provide fast objective functions, gradients, and for some cases hessians as well as approximations thereof. As a user, you can easily define custom loss functions. For those, you can decide to provide analytical gradients or use finite difference approximation /... -
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Bytecoder
Framework to interpret and transpile JVM bytecode to JavaScript
Bytecoder is a Rich Domain Model for Java Bytecode and Framework to interpret and transpile it to other languages such as JavaScript, OpenCL or WebAssembly. The JVM Bytecode is parsed and transformed into an intermediate representation. This intermediate representation is passed thru optimizer stages and sent to a backend implementation for target code generation. The JavaScript backend transforms the intermediate representation into JavaScript. -
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Modcomp XPL Compiler
XPL Compiler for the Modcomp III/IV and Classic
...The first was written in the late 1970's and released to the Modcomp Users Group (MUSE). The second compiler is an extension of the first that includes a Common Sub-Expression optimizer. XPL was used to write the HAL/S compiler for NASA about the same time this XPL compiler was being developed but this is not the compiler used to create HAL/S. I am in need of technical documentation for the Modcomp Classic and the MAX IV operating system. Please let me know in the discussion section is you have documentation from Modular Computer Systems. -
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Battery Optimizer
Helps to improve the life expectancy of an electronic device's battery
...Most electronic devices when charged to 100 percent will continue to receive power from their charger, overtime this will slowly degrade the life expectancy of the lithium ion battery in the electronic device. The Battery Optimizer aims to increase the life expectancy of a lithium ion battery in an electronic device by automatically stopping power being fed to an electronic device when the battery has reached a set threshold. Some companies do this with their devices but are usually proprietary. -Lenovo does this with some of their laptops -Company called "chargie" already doing this for phones. ... -
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FairScale
PyTorch extensions for high performance and large scale training
FairScale is a collection of PyTorch performance and scaling primitives that pioneered many of the ideas now used for large-model training. It introduced Fully Sharded Data Parallel (FSDP) style techniques that shard model parameters, gradients, and optimizer states across ranks to fit bigger models into the same memory budget. The library also provides pipeline parallelism, activation checkpointing, mixed precision, optimizer state sharding (OSS), and auto-wrapping policies that reduce boilerplate in complex distributed setups. Its components are modular, so teams can adopt just the sharding optimizer or the pipeline engine without rewriting their training loop. ... -
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CSSO
CSS minifier with structural optimizations
CSSO (CSS Optimizer) is a CSS minifier. It performs three sorts of transformations: cleaning (removing redundant), compression (replacement for the shorter forms), and restructuring (merge of declarations, rules, and so on). As a result, an output CSS becomes much smaller in size. One of CDN services like unpkg or jsDelivr can be used. By default (for short path) the ESM version is exposed. -
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YOLOV3 Pytorch
This is a source code for yolo3-pytorch
...It includes utilities for annotation conversion, anchor generation, image prediction, video prediction, batch prediction, FPS measurement, heatmap output, and mAP evaluation. The project added multi-GPU training, target count statistics, learning rate scheduling with step and cosine options, and optimizer selection between Adam and SGD. It also includes adaptive learning rate adjustment based on batch size, image cropping, many configurable parameters, and expanded comments for easier study. It is well suited for learners and developers who want a hands-on YOLOv3 codebase in PyTorch. -
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YOLOV4 Pytorch
This is a source code for YoloV4-pytorch that can be used to train you
...The project added multi-GPU training, seed settings for reproducible results, adaptive learning rate behavior based on batch size, and both step and cosine learning rate schedules. It also supports Adam and SGD optimizer choices, image cropping, adjustable parameters, and extensive code comments. It is a useful educational and applied repository for users who want to understand or customize YOLOv4 in PyTorch. -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
...We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the new FullyShardedDataParallel (FSDP) wrapper provided by fairscale. Fairseq can be extended through user-supplied plug-ins. Models define the neural network architecture and encapsulate all of the learnable parameters. Criterions compute the loss function given the model outputs and targets. ... -
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Faster-Rcnn
This is a pytorch implementation library of faster-rcnn
...It supports backbone options through pretrained VGG and ResNet weights, making it useful for comparing feature extractors. The project also includes learning rate scheduling through step and cosine methods, optimizer choices between Adam and SGD, adaptive learning rate behavior based on batch size, image cropping, FPS testing, video prediction, and batch prediction. It is a practical reference for users who want a more classical two-stage detector workflow in PyTorch. -
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DeepLabv3 Plus
Encoder-Decoder with Atrous Separable Convolution
...It provides pretrained weight workflows for MobileNetV2 and Xception backbones and notes that the correct backbone should be selected during training and prediction. The project also supports multi-GPU training, multiple backbones, learning rate schedules with step and cosine options, optimizer selection, and adaptive learning rate behavior based on batch size. It is useful for users who want a stronger semantic segmentation baseline than U-Net for scene-level segmentation tasks. -
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The ASCO project aims to bring circuit optimization capabilities to existing SPICE simulators using a high-performance parallel differential evolution (DE) optimization algorithm. It supports Eldo, HSPICE, LTspice, Spectre, and Qucs.