Source code for the Paper: CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints}

Last update: Dec 27, 2022

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Deep Learning CombOptNet

Overview

CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints

Installation

Run pipenv install (at your own risk with --skip-lock to save some time).
From within the pipenv environment run python3 -m pip install -i https://pypi.gurobi.com gurobipy.
Obtain a license and download/set it.
Download and extract the datasets.

Usage

For [experiment] = knapsack or [experiment] = static_constraints:

Set the base_dataset_path parameter in experiments/[experiment]/base.yaml.
In case of static constraints: set the dataset_specification parameter in experiments/static_constraints/base.yaml
Run python3 main.py experiments/[experiment]/[method].yaml.

Owner

Autonomous Learning Group

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Source code for the Paper: CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints}

Related tags

Overview

CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints

Installation

Usage

Owner

Autonomous Learning Group

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Source code for the Paper: CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints}

Related tags

Overview

CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints

Installation

Usage

Owner

Autonomous Learning Group

Using Tensorflow Object Detection API to detect Waymo open dataset

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Axel - 3D printed robotic hands and they controll with Raspberry Pi and Arduino combo

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