Search Results for "tsp problem parallel"
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Showing 61 open source projects for "tsp problem parallel"
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Hermione
Browser test runner based on mocha and wdio
...If you are familiar with WebdriverIO and Mocha, you can start writing and running tests in 5 minutes! You need to install Hermione via npm and add a tiny config to your project. When tests are run one by one, it takes a lot of time. Hermione can run tests in parallel sessions in different browsers out of the box. Running of too many tests in parallel can lead to the overloading of the main process CPU usage which causes degradation in test passing time, so Hermione runs all tests in subprocesses in order to solve this problem. -
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Claude Octopus
Put up to 8 AI models on every coding task
Claude Octopus is a multi-model orchestration plugin that enables developers to run multiple AI models simultaneously on a single coding task to reduce blind spots and improve output quality. It allows up to eight different models to analyze the same problem in parallel, surfacing diverse perspectives before finalizing results. The system is particularly useful for detecting inconsistencies, edge cases, and errors that a single model might miss. It integrates directly into coding workflows, making it easy to compare outputs without leaving the development environment. The plugin supports multiple providers and model types, enabling flexible combinations of local and cloud-based models. ... -
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AntTSP
Traveling Salesman Problem (TSP) Solver
Fast Ant Colony Optimization TSP Solver -
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Make It heavy
A Python framework that emulates Grok Heavy functionality
Make It heavy is a Python framework for producing deeper AI analysis through multi-agent orchestration. It is designed to emulate the style of Grok Heavy by splitting a user query into several specialized research angles. The system runs four agents in parallel so each one can explore the problem from a different perspective. It then combines their outputs into one unified answer through an intelligent synthesis step. The framework uses OpenRouter for model access and can also run in single-agent mode for simpler tasks. Overall, it is useful for users who want broader research coverage, richer reasoning diversity, and structured multi-agent responses from one command-line workflow. -
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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... -
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GPU Puzzles
Solve puzzles. Learn CUDA
...The exercises are implemented using Python with the Numba CUDA interface, which allows Python code to compile into GPU kernels that run on CUDA-enabled hardware. By solving progressively more complex puzzles, learners gain a practical understanding of how parallel algorithms operate on graphics processing units. The project emphasizes experimentation and problem solving, encouraging learners to discover GPU programming techniques through trial and exploration. It can be run in cloud environments such as Google Colab, making it easy for beginners to start experimenting without configuring local GPU hardware. -
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Fractals
Fractals is a recursive task orchestrator for agent swarm
Fractals is an experimental open-source framework designed to orchestrate complex tasks using swarms of AI agents organized in a recursive structure. The system takes a high-level goal and decomposes it into a hierarchy of smaller subtasks, forming a self-similar tree that resembles a fractal structure. Each leaf node of this tree represents a specific executable task that can be processed independently by an AI agent. The framework runs these subtasks in isolated Git worktrees so agents can... -
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Geodesic
Geodesic is a DevOps Linux Toolbox in Docker
Geodesic is a robust Linux toolbox container, crafted to optimize DevOps workflows. This container comes fully loaded with all essential dependencies for a complete DevOps toolchain. It's designed to bring consistency and boost efficiency across development environments. It achieves this without the need for installing additional software on your workstation. Think of Geodesic as a containerized parallel to Vagrant, offering similar functionality within a Docker container context. -
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LinDB
LinDB is a scalable, high performance, high availability database
...And the tags-based sharding strategy resolves the hotspot problem. LinDB is designed to work under a Multi-Active IDCs cloud architecture. The compute layer of LinDB, called brokers, supports efficient Multi-IDCs aggregation query. -
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PelicanHPC is an iso-hybrid (CD or USB) image that let's you set up a high performance computing cluster in a few minutes. A Pelican cluster allows you to do parallel computing using MPI. You can run Pelican on a single multiple core machine to use all cores to solve a problem, or you can network multiple computers together to make a cluster. The frontend node (either a real computer or a virtual machine) boots from the image. The compute nodes boot by PXE, using the frontend node as the server. ...
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scripture+
The Modern Bible Reader for a Multi-Lingual India
In an era of digital distractions, finding a focused and beautiful space for scripture study can be a challenge. That’s why we built Scripture+—a modern, offline-capable Windows application designed specifically for those who seek a deeper connection with the Word across multiple languages. Whether you are a scholar, a student, or someone who simply wants to read the Bible in your mother tongue, Scripture+ offers a premium experience tailored for discovery and reflection. 🌍 A Library... -
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FEMM_PMSM_GUI_V2 1.FIX Parallel emf voltage problem and ind Calculation 2.add Arkkio torque Calculation 3.Support for segmentation models (sliding band mode only) 4.Supports iron loss calculation(Supported in full electrical cycle and sliding band modes; not supported in standard mode) #Add new FEMM_DXF_SET_Driver FEMM PMSM Motor GUI — V1 A multi-functional desktop GUI that automates finite-element (FEMM) analysis of permanent-magnet synchronous motors (PMSM/IPM). ...
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SpiceDB
SpiceDB is the open source, Zanzibar-inspired authorization system
Open Source, Google Zanzibar-inspired permissions database to enable fine-grained access control for customer applications. Get started with the SpiceDB playground: https://play.authzed.com/ -
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Statistical Rethinking 2023
Statistical Rethinking Course for Jan-Mar 2023
The 2023 edition modernizes and expands on the same curriculum, adjusting exercises and code for newer versions of R, Stan, and supporting packages. It continues to provide scripts for lectures and tutorials, while integrating refinements to examples, notation, and computational workflows introduced that year. Compared with 2022, some models are rewritten for clarity, and teaching materials reflect refinements in McElreath’s evolving presentation of Bayesian data analysis. Students following... -
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Graph of Thoughts
Official Implementation of "Graph of Thoughts
...The framework executes these operations using a large language model as the reasoning engine while evaluating intermediate results to guide the search process. This approach enables models to explore multiple reasoning strategies in parallel and choose the most promising solutions during problem solving. -
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LMAX Disruptor
High performance inter-thread messaging library
LMAX aims to be the fastest trading platform in the world. Clearly, in order to achieve this we needed to do something special to achieve very low-latency and high-throughput with our Java platform. Performance testing showed that using queues to pass data between stages of the system was introducing latency, so we focused on optimising this area. The Disruptor is the result of our research and testing. We found that cache misses at the CPU-level, and locks requiring kernel arbitration are... -
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GeneticSharp
GeneticSharp is a fast, extensible, multi-platform and multithreading
...Can be used in any kind of .NET 6, .NET Standard and .NET Framework apps, like ASP .NET MVC, ASP .NET Core, Blazor, Web Forms, UWP, Windows Forms, GTK#, Xamarin, MAUI and Unity3D games. GeneticSharp and extensions (TSP, AutoConfig, Bitmap equality, Equality equation, Equation solver, Function builder, etc). A Blazor client application template with GeneticSharp ready to run a Travelling Salesman Problem (TSP). A console application template with GeneticSharp, you just need to implement the chromosome and fitness function. A console application template with GeneticSharp ready to run a Travelling Salesman Problem (TSP). -
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Statistical Rethinking 2022
Statistical Rethinking course winter 2022
...The code emphasizes Bayesian data analysis using R, the rethinking package, and Stan models. It includes lecture code files, example datasets, and structured exercises that parallel the topics covered in the lectures (probability, regression, model comparison, Bayesian updating). The repo functions as a direct hands-on reference for students following the 2022 recorded lecture series. There are 10 weeks of instruction. Links to lecture recordings will appear in this table. Weekly problem sets are assigned on Fridays and due the next Friday, when we discuss the solutions in the weekly online meeting. -
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Cuda Simulated Annealing GPU Route Plan
An Optimized GPU-Accelerated Route Planning of Multi-UAV Systems Using
...In order to obtain better performance, in the literature, most of the researchers propose using evolutionary algorithms and artificial intelligence approaches based on heuristics as optimization techniques. In addition to this, parallel programming approaches increase the computation performance. Therefore, this study focuses to discuss and solve the route planning problem for multi-UAV systems by using optimization techniques based on an evolutionary algorithm: simulated annealing. The travel cost and execution time are downsized in this work by optimization on algorithm and code. ... -
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scikit-opt
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing
scikit-opt is a Python library for solving optimization problems with evolutionary and swarm-intelligence algorithms. It includes genetic algorithms, particle swarm optimization, differential evolution, simulated annealing, ant colony optimization, immune algorithms, and artificial fish swarms. The package can address continuous objectives, constrained problems, and combinatorial tasks such as the traveling salesman problem. A consistent workflow lets users define an objective, configure an... -
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DETR
End-to-end object detection with transformers
...Due to this parallel nature, DETR is very fast and efficient. -
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jsprit
Open source toolkit for solving rich vehicle routing problems
jsprit is a java based, open-source toolkit for solving rich Traveling Salesman Problems(TSP) and Vehicle Routing Problems(VRP). It is lightweight, flexible and easy-to-use, and based on a single all-purpose meta-heuristic. Setting up the problem, defining additional constraints, modifying the algorithms and visualizing the discovered solutions is as easy and handy as reading classical VRP instances to benchmark your algorithm. -
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The J-Horizon is java based vehicle Routing problem software that uses the jsprit library to solve: Capacitated VRP, Multiple Depot VRP, VRP with Time Windows, VRP with Backhauls, VRP with Pickups and Deliveries, VRP with Homogeneous or Heterogeneous Fleet, VRP with Open or Closed routes, TSP, mTSP and various combination of these types. Latitude/Longitude is also supported.
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coarrays
A free Fortran 2008, 2018 coarrays course with notes and exercises
...All course materials are released under BSD license. We welcome contributions, provided you are happy to release your work under BSD license. We welcome comments and problem reports. -
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TSP Solver and Generator
Generate and solve Travelling Salesman Problem tasks
TSPSG is intended to generate and solve Travelling Salesman Problem (TSP) tasks. It uses Branch and Bound method for solving. An input is a number of cities and a matrix of city-to-city travel prices. The matrix can be populated with random values in a given range (useful for generating tasks). The result is an optimal route, its price, step-by-step matrices of solving and solving graph. The task can be saved in internal binary format and opened later.