gradient-methods

# Gradient Methods ## When to Use Use this skill when working on gradient-methods problems in optimization. ## Decision Tree 1. **Basic Gradient Descent** - Update: x_{k+1} = x_k - alpha * grad f(x_k) - Step size alpha: fixed, diminishing, or line search - Convergence: O(1/k) for convex, linear for strongly convex 2. **Step Size Selection** | Method | Approach | |--------|----------| | Fixed | alpha constant (requires tuning) | | Backtracking | Armijo condition: f(x - alpha*grad) <= f(x) - c*alpha*||grad||^2 | | Exact line search | minimize f(x - alpha*grad) over alpha | | Adaptive | Adam, RMSprop (ML applications) | 3. **Accelerated Methods** - Momentum: add velocity term - Nesterov: look-ahead gradient - Conjugate gradient: for quadratic functions - `scipy.optimize.minimize(f, x0, method='CG')` - conjugate gradient 4. **Newton's Method** - Update: x_{k+1} = x_k - H^{-1} * grad f - Requires Hessian (expensive but quadratic convergence) - Quasi-Newton (BFGS): approximate Hessian - `scipy.optimize.minimize(f, x0, method='BFGS')` 5. **Convergence Diagnostics** - Monitor ||grad f|| < tolerance - Check function value decrease - Watch for oscillation (step size too large) - `sympy_compute.py diff "f" --var x` for gradient ## Tool Commands ### Scipy_Bfgs ```bash uv run python -c "from scipy.optimize import minimize; res = minimize(lambda x: (x[0]-1)**2 + 100*(x[1]...