A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

Last update: Jan 18, 2022

Overview

Mixed-Integer-Linear-Programming

A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

We seek to determine the best possible division of 14 shires on agiven map into 5 constituencies, each composed of one or more shires. We do so towards the goal of maximising the number of such constituencies in which “Joris Bohnson” wins a majority of the total votes inside this constituency. As a result, he is able to send the maximum possible of representatives to Parliament.

Within each shire, the total number of voters and the total number of votes forJoris are already set. As such, we need only draw the boundaries upon the mapin order to secure representatives. We do so subject to constraints regarding what makes a “valid” constituency, and the desire to maintain appearances by not allowing shire 10 to become a singleton (one shire) constituency.

Owner

Emma

MSc Operational Research and Statistics student at the University of Southampton (she / her)

GitHub Repository

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A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

Related tags

Overview

Mixed-Integer-Linear-Programming

Owner

Emma

ERISHA is a mulitilingual multispeaker expressive speech synthesis framework. It can transfer the expressivity to the speaker's voice for which no expressive speech corpus is available.

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A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

Related tags

Overview

Mixed-Integer-Linear-Programming

Owner

Emma

ERISHA is a mulitilingual multispeaker expressive speech synthesis framework. It can transfer the expressivity to the speaker's voice for which no expressive speech corpus is available.

DeLag: Detecting Latency Degradation Patterns in Service-based Systems

Efficiently Disentangle Causal Representations

This is the codebase for the ICLR 2021 paper Trajectory Prediction using Equivariant Continuous Convolution

RL and distillation in CARLA using a factorized world model

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Official PyTorch implementation of "Improving Face Recognition with Large AgeGaps by Learning to Distinguish Children" (BMVC 2021)

Reaction SMILES-AA mapping via language modelling

Make Watson Assistant send messages to your Discord Server

Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN)

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Code in both PyTorch and TensorFlow

This repo provides the official code for TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/pdf/2103.04430.pdf).

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