docs: Rebrand OptimizR to Optimiz-rs throughout documentation

Updated Branding in ReadTheDocs:
- ✅ All 'OptimizR' → 'Optimiz-rs' (17 files)
- ✅ Project name in conf.py
- ✅ HTML title and short title
- ✅ All algorithm documentation
- ✅ Getting started guide
- ✅ Installation guide
- ✅ Theory/mathematical foundations
- ✅ Archive documentation

Documentation now consistently uses the new 'optimiz-rs' branding that
matches both PyPI and crates.io package names.

Note: Python module name 'optimizr' in import statements intentionally
unchanged (that's the actual module name).

docs/source/algorithms/differential_evolution.md

@@ -303,7 +303,7 @@ print(f"Function evaluations: {result.nfev}")
 
 ## Adaptive Control (jDE)
 
-OptimizR implements **jDE** (self-adaptive DE), where the parameters $F$ and $CR$ 
+Optimiz-rs implements **jDE** (self-adaptive DE), where the parameters $F$ and $CR$ 
 evolve with the population:
 
 $$

docs/source/algorithms/mcmc.md

@@ -161,7 +161,7 @@ $$
 
 This is symmetric, so Metropolis acceptance applies.
 
-**This is what OptimizR implements.**
+**This is what Optimiz-rs implements.**
 
 ---
 

docs/source/conf.py

@@ -6,7 +6,7 @@ import sys
 sys.path.insert(0, os.path.abspath('../../python'))
 
 # -- Project information -----------------------------------------------------
-project = 'OptimizR'
+project = 'Optimiz-rs'
 copyright = '2026, HFThot Research Lab'
 author = 'HFThot Research Lab'
 release = '0.3.0'
@@ -32,8 +32,8 @@ exclude_patterns = []
 # -- Options for HTML output -------------------------------------------------
 html_theme = 'furo'  # Modern, clean theme
 html_static_path = ['_static']
-html_title = 'OptimizR Documentation'
-html_short_title = 'OptimizR'
+html_title = 'Optimiz-rs Documentation'
+html_short_title = 'Optimiz-rs'
 html_logo = 'logo_optimizrs.png'
 html_favicon = 'logo_optimizrs.png'
 

docs/source/examples.md

@@ -1,6 +1,6 @@
 # Examples
 
-Practical snippets for every OptimizR component.
+Practical snippets for every Optimiz-rs component.
 
 ## Differential Evolution (global optimization)
 

docs/source/getting-started.md

@@ -12,7 +12,7 @@ pip install -r docs/requirements.txt
 pip install maturin numpy
 ```
 
-## 2. Build and install OptimizR locally
+## 2. Build and install Optimiz-rs locally
 
 ```bash
 pip install .
@@ -26,7 +26,7 @@ maturin develop --release
 python - <<'PY'
 import optimizr
 from optimizr import differential_evolution, HMM
-print("OptimizR version:", optimizr.__version__)
+print("Optimiz-rs version:", optimizr.__version__)
 
 # Simple objective
 f = lambda x: sum(v * v for v in x)

docs/source/index.rst

@@ -1,6 +1,6 @@
-.. OptimizR documentation master file
+.. Optimiz-rs documentation master file
 
-OptimizR Documentation
+Optimiz-rs Documentation
 ======================
 
 **High-performance optimization algorithms in Rust with Python bindings**
@@ -13,7 +13,7 @@ OptimizR Documentation
    :target: https://github.com/ThotDjehuty/optimiz-r/blob/main/LICENSE
    :alt: License
 
-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.
 
 .. toctree::
    :maxdepth: 2

docs/source/installation.md

@@ -8,7 +8,7 @@
 
 ## Install from PyPI
 
-**Coming soon**: OptimizR will be available on PyPI.
+**Coming soon**: Optimiz-rs will be available on PyPI.
 
 ```bash
 pip install optimizr

docs/source/theory/mathematical_foundations.md

@@ -1,6 +1,6 @@
 # Mathematical Foundations
 
-This page collects the core equations driving OptimizR’s Rust kernels, plus short intuition blurbs and micro-checks you can run in a notebook. For visuals and full walkthroughs, see the example notebooks in `examples/notebooks/`.
+This page collects the core equations driving Optimiz-rs’s Rust kernels, plus short intuition blurbs and micro-checks you can run in a notebook. For visuals and full walkthroughs, see the example notebooks in `examples/notebooks/`.
 
 ## Differential Evolution (DE)
 
@@ -29,7 +29,7 @@ $$
 \end{cases}
 $$
 
-**Self-adaptive jDE (used by OptimizR):**
+**Self-adaptive jDE (used by Optimiz-rs):**
 $$
 F_i^{g+1} = \begin{cases}
 F_{\min} + r_1 \cdot F_{\max} & r_2 < \tau_1,\\
@@ -52,7 +52,7 @@ $$
 -\partial_t V(t,x) = \inf_{u\in\mathcal{U}} \Big[ \ell(x,u) + \nabla_x V(t,x)^{\top} b(x,u) + \tfrac12 \operatorname{Tr}\big(\sigma\sigma^{\top}(x,u) \, \nabla_x^2 V(t,x)\big) \Big],\quad V(T,x) = g(x).
 $$
 
-OptimizR uses finite differences with backward time-stepping and optional policy iteration. On a uniform grid $(t_n, x_j)$:
+Optimiz-rs uses finite differences with backward time-stepping and optional policy iteration. On a uniform grid $(t_n, x_j)$:
 $$
 V^{n} = \min_{u}\Big\{ \ell(x_j,u)\,\Delta t + V^{n+1} + \nabla_x V^{n+1}\cdot b\,\Delta t + \tfrac12 \operatorname{Tr}(\sigma\sigma^{\top}\nabla_x^2 V^{n+1})\,\Delta t \Big\}.
 $$
@@ -62,7 +62,7 @@ The control that attains the minimum yields the feedback policy $u^{\star}(x_j,
 
 ## Mean Field Games (1D solver)
 
-OptimizR’s MFG module solves the coupled system for value $u$ and density $m$:
+Optimiz-rs’s MFG module solves the coupled system for value $u$ and density $m$:
 $$
 \begin{aligned}
 -\partial_t u(t,x) - \nu\,\partial_{xx} u(t,x) + H\big(x,\partial_x u(t,x), m(t,x)\big) &= 0,\\
@@ -100,7 +100,7 @@ For target density $\pi(x)$ and proposal $q(x'\mid x)$:
 $$
 \alpha(x \to x') = \min\Big(1, \frac{\pi(x')\, q(x \mid x')}{\pi(x)\, q(x' \mid x)}\Big).
 $$
-OptimizR uses symmetric Gaussian proposals (so $q$ cancels) by default, with optional bounds projection and burn-in.
+Optimiz-rs uses symmetric Gaussian proposals (so $q$ cancels) by default, with optional bounds projection and burn-in.
 
 **Heuristic:** Tune proposal std so acceptance is ~0.25–0.35 for moderate dimensions; see `examples/notebooks/02_mcmc.ipynb` for trace plots.