Question
Perform one full-batch gradient-descent update step for simple linear regression y = w*x + b under mean-squared error. Given parallel lists xs and ys (n>=1 points), current weight w, bias b, and learning rate lr, compute predictions p_i = w*x_i + b, the error e_i = p_i - y_i, the gradients dw = (2/n)*sum(e_i*x_i) and db = (2/n)*sum(e_i), then return the updated parameters [w - lr*dw, b - lr*db]. Round each returned parameter to 6 decimal places.
gd_step(xs: list[float], ys: list[float], w: float, b: float, lr: float) → list[float][[1,2],[2,4],0,0,0.1]out[1,0.6]State your approach and its time/space complexity out loud before you optimize. Handle the edge cases (empty input, duplicates, overflow), and say why you chose this over the brute force. Green tests are the floor, not the grade.
Vibe coding: describe the solution in plain language (or narrate it) and the coach grades your approach. Generating runnable code from your description is coming next.