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fix: Update PyPI metadata with Optimiz-rs branding and logo (v1.0.1)

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- Replace all OptimizR references with Optimiz-rs in README
- Fix logo URL to use GitHub raw link (displays on PyPI)
- Bump version to 1.0.1 for metadata update
- Published to PyPI: https://pypi.org/project/optimiz-rs/1.0.1/

Changes:
- README.md: 7 instances of OptimizR → Optimiz-rs
- README.md: Logo URL now uses raw.githubusercontent.com
- pyproject.toml: version 1.0.0 → 1.0.1
- Cargo.toml: version 1.0.0 → 1.0.1
This commit is contained in:
ThotDjehuty
2026-02-17 10:22:42 +01:00
parent b10263b4f2
commit efde8d19a5
3 changed files with 11 additions and 11 deletions
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Cargo.toml
+1 -1
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@@ -1,6 +1,6 @@
[package]
name = "optimiz-rs"
version = "1.0.0"
version = "1.0.1"
edition = "2021"
authors = ["HFThot Research Lab <contact@hfthot-lab.eu>"]
description = "High-performance optimization algorithms in Rust with Python bindings"
README.md
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@@ -1,7 +1,7 @@
# OptimizR 🚀
# Optimiz-rs 🚀
<p align="center">
<img src="docs/source/logo_optimizrs.png" alt="OptimizR Logo" width="220" />
<img src="https://raw.githubusercontent.com/ThotDjehuty/optimiz-r/main/docs/source/logo_optimizrs.png" alt="Optimiz-rs Logo" width="220" />
</p>
**High-performance optimization algorithms in Rust with Python bindings**
@@ -11,7 +11,7 @@
[![Rust](https://img.shields.io/badge/rust-1.70+-orange.svg)](https://www.rust-lang.org/)
[![Python](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/)
OptimizR provides blazingly fast, production-ready implementations of advanced optimization and statistical inference algorithms. Built with Rust for maximum performance and exposed to Python through PyO3, it delivers 50-100× speedup over pure Python implementations.
Optimiz-rs provides blazingly fast, production-ready implementations of advanced optimization and statistical inference algorithms. Built with Rust for maximum performance and exposed to Python through PyO3, it delivers 50-100× speedup over pure Python implementations.
## ✨ What's New in v1.0.0
@@ -433,8 +433,8 @@ Hamilton-Jacobi-Bellman equation solvers for stochastic control:
Comparison against pure Python/NumPy/SciPy implementations (v0.2.0):
| Algorithm | Problem Size | OptimizR (Rust) | NumPy/SciPy | Speedup |
|-----------|--------------|-----------------|-------------|---------|
| Algorithm | Problem Size | Optimiz-rs (Rust) | NumPy/SciPy | Speedup |
|-----------|--------------|-------------------|-------------|---------|
| **DE - rand/1** | 50D Rosenbrock | 285ms | 21.2s | **74×** |
| **DE - best/1** | 50D Rosenbrock | 270ms | 23.8s | **88×** |
| **DE - adaptive jDE** | 50D Rosenbrock | 310ms | 24.5s | **79×** |
@@ -504,7 +504,7 @@ The documentation includes:
- 💡 **Examples** - Real-world use cases and code samples
- ⚡ **Performance** - Benchmarks and optimization tips
**New to OptimizR?** Start with the [Quick Start Guide](https://optimiz-r.readthedocs.io/en/latest/quickstart.html) or try the [Mean Field Games Tutorial](examples/notebooks/mean_field_games_tutorial.ipynb).
**New to Optimiz-rs?** Start with the [Quick Start Guide](https://optimiz-r.readthedocs.io/en/latest/quickstart.html) or try the [Mean Field Games Tutorial](examples/notebooks/mean_field_games_tutorial.ipynb).
## Development
@@ -566,11 +566,11 @@ MIT License - see [LICENSE](LICENSE) file for details.
## Citation
If you use OptimizR in your research, please cite:
If you use Optimiz-rs in your research, please cite:
```bibtex
@software{optimizr2024,
title = {OptimizR: High-Performance Optimization Algorithms in Rust},
title = {Optimiz-rs: High-Performance Optimization Algorithms in Rust},
author = {HFThot Research Lab},
year = {2024},
version = {1.0.0},
@@ -601,4 +601,4 @@ Inspired by:
---
**OptimizR** - Fast optimization for data science and machine learning 🚀
**Optimiz-rs** - Fast optimization for data science and machine learning 🚀
pyproject.toml
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@@ -4,7 +4,7 @@ build-backend = "maturin"
[project]
name = "optimiz-rs"
version = "1.0.0"
version = "1.0.1"
description = "High-performance optimization algorithms in Rust with Python bindings"
authors = [
{name = "HFThot Research Lab", email = "contact@hfthot-lab.eu"}