| #!/usr/bin/python3.12 | |
| def visualize_quadtree(quadtree, points, query_rect=None, found_points=None, | |
| filename="quadtree", title="Quadtree Visualization"): | |
| """Visualize the quadtree structure with points.""" | |
| fig, ax = plt.subplots(figsize=(12, 12)) | |
| # Draw all quadtree boundaries | |
| boundaries = quadtree.get_all_boundaries() | |
| for boundary in boundaries: | |
| x = boundary.x - boundary.width/2 |
This project demonstrates global optimization techniques using SciPy on the Eggholder function, a complex mathematical function commonly used for testing optimization algorithms.
The Eggholder function is a challenging optimization problem with many local minima, making it an excellent benchmark for testing global optimization algorithms.
Global minimum: f(x*) = -959.6407, at x* = (512, 404.2319)
| { | |
| "version": "1.2", | |
| "variables": [ | |
| {"label":"X1", "interface":"knob", "type":"real", "range":[0,5], "rad-abs": 0.0}, | |
| {"label":"X2", "interface":"knob", "type":"real", "range":[0,3], "rad-abs": 0.0}, | |
| {"label":"F1", "interface":"output", "type":"real"}, | |
| {"label":"F2", "interface":"output", "type":"real"} | |
| ], | |
| "alpha": "(X1-5)*(X1-5)+X2*X2-25 and -(X1-8)*(X1-8)-(X2+3)*(X2+3)+7.7", | |
| "objectives": { |
| #!/usr/bin/python3.14 | |
| # -*- coding: utf-8 -*- | |
| import torch | |
| import math | |
| from hashlib import sha256 | |
| from matplotlib import pyplot as plt | |
| def main(timeout: int=5000, size: int=100000) -> int: | |
| dtype = torch.float | |
| device = torch.device("cpu") |
| import pytest | |
| # Mark all tests in this directory to run forked | |
| def pytest_collection_modifyitems(items): | |
| for item in items: | |
| # Check if test is in this directory | |
| if "z3" in str(item.fspath): | |
| item.add_marker(pytest.mark.forked) | |
| # Configure logging for forked tests |