Calculate the gradient (vector of partial derivatives) of a multi-variable function at a given point. The gradient points in the direction of steepest ascent and is essential for optimization algorithms, sensitivity analysis, and machine learning applications. Uses finite differences to approximate each partial derivative.
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Built-in multi-variable function name
rosenbrock, sphere, rastrigin, quadratic "rosenbrock"
Point at which to evaluate the gradient (vector of coordinates)
[1, 1]Step size for finite difference
1e-7
Finite difference method
forward, central "forward"