result, error = core.safe_divide(10, 3) print(f"10/3 = result ± error") # Output: 10/3 = 3.3333333333333335 ± 1.11e-16
def arithmetic_sequence(start: float, diff: float, n: int) -> List[float]: """Generate first n terms of arithmetic sequence.""" return [start + i * diff for i in range(n)] danlwd grindeq math utilities
def geometric_sequence(start: float, ratio: float, n: int) -> List[float]: """Generate first n terms of geometric sequence.""" return [start * (ratio ** i) for i in range(n)] result, error = core
def fibonacci(n: int) -> List[int]: """Return first n Fibonacci numbers.""" if n <= 0: return [] if n == 1: return [0] fib = [0, 1] for _ in range(2, n): fib.append(fib[-1] + fib[-2]) return fib[:n] Root finding:
Risk models, option pricing (Black-Scholes), and portfolio optimization require heavy numerical lifting. The grindeq.optim module provides fast convergence for mean-variance optimization, while grindeq.linalg handles covariance matrix decomposition with near-zero numerical drift.
Example: A hedge fund using Danlwd Grindeq could run 10,000 Monte Carlo simulations of a derivatives portfolio in under 2 seconds, with each simulation reporting precise error margins.