Databytes on Instagram: "Gradient Descent can fail when an update step jumps outside the valid domain, a classic out of domain problem in univariate optimization. Large learning rates push parameters beyond allowed ranges, so boundary checks step control and advanced GD variants keep updates stable and meaningful. [out of domain gradient descent, univariate optimization, learning rate too high, update step overflow, boundary constraints, optimization failure, convex function limits, step size co