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https://github.com/kylesayrs/distance-optimization
Given a list of points and target distances between them, this algorithm optimizes their positions with respect to MSE loss.
https://github.com/kylesayrs/distance-optimization
Last synced: 9 days ago
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Given a list of points and target distances between them, this algorithm optimizes their positions with respect to MSE loss.
- Host: GitHub
- URL: https://github.com/kylesayrs/distance-optimization
- Owner: kylesayrs
- Created: 2022-09-09T00:24:30.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-23T05:47:04.000Z (about 1 year ago)
- Last Synced: 2023-11-23T06:30:03.849Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 20.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Distance Optimization #
Given a list of points and target distances between them, this algorithm uses gradient descent to find an optimal configuration of points in N-dimensional space. This can be used by urban planners for optimizing building placement or roboticists for approximate localization.# Convex Optimization #
This problem can be uniquely solved as a convex problem with margin for error (see [convex_optimization](./convex_optimization/)).# Gradient Descent #
This problem is also an interesting exercise in gradient descent (see [gradient_descent](./gradient_descent/)).