Add make docs commands for html build

docs/source/algorithms/sparse_optimization.md

@@ -1,6 +1,6 @@
 # Sparse Optimization
 
-Sparse PCA and Elastic Net utilities with Rust speed.
+Sparse PCA, Elastic Net, and Box–Tao decomposition with Rust speed.
 
 ## Sparse PCA
 
@@ -13,6 +13,9 @@ components = sparse_pca_py(X, n_components=5, l1_ratio=0.15)
 print(components.shape)  # (5, 20)
 ```
 
+- Output: component matrix `(n_components, n_features)`; rows are sparse loadings.
+- Tuning: increase `l1_ratio` for harder sparsity; decrease to retain variance.
+
 ## Elastic Net
 
 ```python
@@ -25,7 +28,19 @@ coeffs = elastic_net_py(X, y, l1_ratio=0.3, alpha=0.01)
 print(coeffs)
 ```
 
-## Notes
-- Inputs should be NumPy arrays; data is copied to Rust.
-- `l1_ratio` balances sparsity vs ridge penalty.
-- Standardize features before calling for stable solutions.
+- Handles collinearity better than pure Lasso; use for factor shrinkage.
+- Sweep `alpha` on a log scale (e.g., $10^{-3}$ to $10^{-1}$) and pick via validation.
+
+## Box–Tao decomposition
+
+```python
+from optimizr import box_tao_decomposition_py
+solution = box_tao_decomposition_py(X)
+```
+
+Useful for constrained sparse decomposition problems; the Rust backend keeps iterations fast.
+
+## Practical notes
+- Inputs must be NumPy arrays; standardize features for stable conditioning.
+- For high dimensional data, start with fewer components/features to avoid over-regularization.
+- Combine with risk metrics: use sparse loadings to build interpretable factors, then evaluate with `compute_risk_metrics_py`.