Add Muon optimizer implementation

- Implements Muon optimizer for hidden layer weight matrices
- Uses Newton-Schulz orthogonalization iterations
- Provides matrix-aware gradient updates with spectral constraints
- Includes comprehensive docstrings and type hints
- Adds doctests for validation
- Provides usage example demonstrating optimization
- Follows PEP8 coding standards
- Pure NumPy implementation without frameworks
- Part of issue #13662

Author: Adhithya-Laxman

neural_network/optimizers/muon.py

@@ -0,0 +1,117 @@
+"""
+Muon Optimizer
+
+Implements Muon optimizer for neural network hidden layers using NumPy.
+Muon uses Newton-Schulz orthogonalization iterations for improved convergence.
+
+Reference: https://kellerjordan.github.io/posts/muon/
+Author: Adhithya Laxman Ravi Shankar Geetha
+Date: 2025.10.21
+"""
+
+import numpy as np
+
+
+class Muon:
+    """
+    Muon optimizer for hidden layer weight matrices.
+
+    Applies Newton-Schulz orthogonalization to gradients before updates.
+    """
+
+    def __init__(
+        self, learning_rate: float = 0.02, momentum: float = 0.95, ns_steps: int = 5
+    ) -> None:
+        """
+        Initialize Muon optimizer.
+
+        Args:
+            learning_rate (float): Learning rate for updates.
+            momentum (float): Momentum factor.
+            ns_steps (int): Number of Newton-Schulz iteration steps.
+
+        >>> optimizer = Muon(learning_rate=0.02, momentum=0.95, ns_steps=5)
+        >>> optimizer.momentum
+        0.95
+        """
+        self.learning_rate = learning_rate
+        self.momentum = momentum
+        self.ns_steps = ns_steps
+        self.velocity: dict[int, np.ndarray] = {}
+
+    def newton_schulz_orthogonalize(self, matrix: np.ndarray) -> np.ndarray:
+        """
+        Orthogonalize matrix using Newton-Schulz iterations.
+
+        Args:
+            matrix (np.ndarray): Input matrix.
+
+        Returns:
+            np.ndarray: Orthogonalized matrix.
+
+        >>> optimizer = Muon()
+        >>> mat = np.array([[1.0, 0.5], [0.5, 1.0]])
+        >>> orth = optimizer.newton_schulz_orthogonalize(mat)
+        >>> orth.shape
+        (2, 2)
+        """
+        if matrix.shape[0] < matrix.shape[1]:
+            matrix = matrix.T
+            transposed = True
+        else:
+            transposed = False
+
+        a = matrix.copy()
+        for _ in range(self.ns_steps):
+            a = 1.5 * a - 0.5 * a @ (a.T @ a)
+
+        return a.T if transposed else a
+
+    def update(
+        self, param_id: int, params: np.ndarray, gradients: np.ndarray
+    ) -> np.ndarray:
+        """
+        Update parameters using Muon.
+
+        Args:
+            param_id (int): Unique identifier for parameter group.
+            params (np.ndarray): Current parameters.
+            gradients (np.ndarray): Gradients of parameters.
+
+        Returns:
+            np.ndarray: Updated parameters.
+
+        >>> optimizer = Muon(learning_rate=0.1, momentum=0.9)
+        >>> params = np.array([[1.0, 2.0], [3.0, 4.0]])
+        >>> grads = np.array([[0.1, 0.2], [0.3, 0.4]])
+        >>> updated = optimizer.update(0, params, grads)
+        >>> updated.shape
+        (2, 2)
+        """
+        if param_id not in self.velocity:
+            self.velocity[param_id] = np.zeros_like(params)
+
+        ortho_grad = self.newton_schulz_orthogonalize(gradients)
+        self.velocity[param_id] = self.momentum * self.velocity[param_id] + ortho_grad
+
+        return params - self.learning_rate * self.velocity[param_id]
+
+
+# Usage example
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
+
+    print("Muon Example: Optimizing a 2x2 matrix")
+
+    optimizer = Muon(learning_rate=0.05, momentum=0.9)
+    weights = np.array([[1.0, 2.0], [3.0, 4.0]])
+
+    for step in range(10):
+        gradients = 0.1 * weights  # Simplified gradient
+        weights = optimizer.update(0, weights, gradients)
+        if step % 3 == 0:
+            print(f"Step {step}: weights =\n{weights}")
+
+    print(f"Final weights:\n{weights}")