This is the source code of the solver used to compete in the International Timetabling Competition 2019.

Related tags

Deep Learningitc-2019
Overview

ITC2019 Solver

This is the source code of the solver used to compete in the International Timetabling Competition 2019.

Building

.NET Core (2.1 or higher) is required to build the solver.

Common build targets are provided in the Makefile: linux-x64, win-x64, osx-x64.

To build standalone binaries, run either of the following commands:

$ make linux
$ make win
$ make osx

# Builds all targets
$ make all # or just make

Output is written to bin/{platform}.

Running

The executable file Timetabling.CLI accepts the following arguments:

USAGE: Timetabling.CLI [--help] --instance 
   
     [--solution 
    
     ] [--seed 
     
      ]

OPTIONS:

    --instance 
      
            XML problem path.
    --solution 
       
         Solution to reload. --seed 
        
          Seed number. 
        
       
      
     
    
   

Shortcut scripts are provided in the repo root: run-linux.sh, run-win.cmd, run-osx.sh.

Example:

./run-linux.sh --instance /path/to/wbg-fal10.xml

The solver will routinely print stats and save solution backups. Sending Control+C will stop the solver and save the best solution.

Solutions are saved relative to the working directory with format solution_ _ .xml .

License

MIT License

Owner
Edon Gashi
Edon Gashi
shufflev2-yolov5:lighter, faster and easier to deploy

shufflev2-yolov5: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size

pogg 1.5k Jan 05, 2023
Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering (NAACL 2021)

Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering Abstract In open-domain question answering (QA), retrieve-and-read mec

Clova AI Research 34 Apr 13, 2022
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped

CSWin-Transformer This repo is the official implementation of "CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows". Th

Microsoft 409 Jan 06, 2023
Semi-automated OpenVINO benchmark_app with variable parameters

Semi-automated OpenVINO benchmark_app with variable parameters. User can specify multiple options for any parameters in the benchmark_app and the progam runs the benchmark with all combinations of gi

Yasunori Shimura 8 Apr 11, 2022
使用深度学习框架提取视频硬字幕;docker容器免安装深度学习库,使用本地api接口使得界面和后端识别分离;

extract-video-subtittle 使用深度学习框架提取视频硬字幕; 本地识别无需联网; CPU识别速度可观; 容器提供API接口; 运行环境 本项目运行环境非常好搭建,我做好了docker容器免安装各种深度学习包; 提供windows界面操作; 容器为CPU版本; 视频演示 https

歌者 16 Aug 06, 2022
Rax is a Learning-to-Rank library written in JAX

🦖 Rax: Composable Learning to Rank using JAX Rax is a Learning-to-Rank library written in JAX. Rax provides off-the-shelf implementations of ranking

Google 247 Dec 27, 2022
Code and datasets for TPAMI 2021

SkeletonNet This repository constains the codes and ShapeNetV1-Surface-Skeleton,ShapNetV1-SkeletalVolume and 2d image datasets ShapeNetRendering. Plea

34 Aug 15, 2022
AI-UPV at IberLEF-2021 EXIST task: Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models

AI-UPV at IberLEF-2021 EXIST task: Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models Descrip

Angel de Paula 1 Jun 08, 2022
TensorFlow Implementation of Unsupervised Cross-Domain Image Generation

Domain Transfer Network (DTN) TensorFlow implementation of Unsupervised Cross-Domain Image Generation. Requirements Python 2.7 TensorFlow 0.12 Pickle

Yunjey Choi 864 Dec 30, 2022
An Intelligent Self-driving Truck System For Highway Transportation

Inceptio Intelligent Truck System An Intelligent Self-driving Truck System For Highway Transportation Note The code is still in development. OS requir

InceptioResearch 11 Jul 13, 2022
Object detection and instance segmentation toolkit based on PaddlePaddle.

Object detection and instance segmentation toolkit based on PaddlePaddle.

9.3k Jan 02, 2023
Code for our paper Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation

CorDA Code for our paper Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation Prerequisite Please create and activate the follo

Qin Wang 60 Nov 30, 2022
Funnels: Exact maximum likelihood with dimensionality reduction.

Funnels This repository contains the code needed to reproduce the experiments from the paper: Funnels: Exact maximum likelihood with dimensionality re

2 Apr 21, 2022
A large-scale video dataset for the training and evaluation of 3D human pose estimation models

ASPset-510 (Australian Sports Pose Dataset) is a large-scale video dataset for the training and evaluation of 3D human pose estimation models. It contains 17 different amateur subjects performing 30

Aiden Nibali 25 Jun 20, 2021
GAN JAX - A toy project to generate images from GANs with JAX

GAN JAX - A toy project to generate images from GANs with JAX This project aims to bring the power of JAX, a Python framework developped by Google and

Valentin Goldité 14 Nov 29, 2022
Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling

⚠️ ‎‎‎ A more recent and actively-maintained version of this code is available in ivadomed Stacked Hourglass Network with a Multi-level Attention Mech

Reza Azad 14 Oct 24, 2022
Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks.

The Lottery Ticket Hypothesis for Pre-trained BERT Networks Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks. [NeurIPS

VITA 122 Dec 14, 2022
PyGRANSO: A PyTorch-enabled port of GRANSO with auto-differentiation

PyGRANSO PyGRANSO: A PyTorch-enabled port of GRANSO with auto-differentiation Please check https://ncvx.org/PyGRANSO for detailed instructions (introd

SUN Group @ UMN 26 Nov 16, 2022
Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way

HackerMath for Machine Learning “Study hard what interests you the most in the most undisciplined, irreverent and original manner possible.” ― Richard

Amit Kapoor 1.4k Dec 22, 2022
Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

PyTorch Implementation of Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers 1 Using Colab Please notic

Hila Chefer 489 Jan 07, 2023