∫Calc Practice

Maximum rate of change

Problem 10.386 · medium

Find the maximum rate of change of \( \displaystyle f(x, y) = x e^{- y} \) at \( \displaystyle (-1, 2) \), and the direction in which it occurs.
  1. \[ \left[\begin{matrix}\frac{\partial}{\partial x} x e^{- y}\\\frac{\partial}{\partial y} x e^{- y}\end{matrix}\right] = \left[\begin{matrix}e^{- y}\\- x e^{- y}\end{matrix}\right] \]
    ∇f.✓ Proved
  2. \[ \left[\begin{matrix}e^{-2}\\e^{-2}\end{matrix}\right] \]
    ∇f(-1, 2).✓ Proved
  3. \[ \frac{\sqrt{2}}{e^{2}} \]
    The maximum rate of change is ‖∇f‖, in the direction of ∇f.✓ Proved
Answer \( \|\nabla f\| = \frac{\sqrt{2}}{e^{2}}\ \text{in the direction of}\ \left\langle e^{-2}, e^{-2} \right\rangle \)

Every line of this solution was proved by the computer algebra system SymPy. The answer was also checked a second way, without looking at the solution. The reviewers disagree about how one step is explained; every verdict is in the receipt.

The full receipt
LineStatusChecked byDetail
1✓ Provedsympy 1.14.0simplify(lhs - rhs) reduced to 0
2✓ Provedsympy 1.14.0simplify(lhs - rhs) reduced to 0
3✓ Provedsympy 1.14.0simplify(lhs - rhs) reduced to 0
answer, a second way✓ Checked independentlysympy 1.14.0 + mpmath 1.3.0the largest directional difference quotient over 3600 directions

Reviewers

  • gpt-oss:20b: fail (misleading) — The direction of maximum increase should be the unit vector in the direction of the gradient, i.e. <1/√2, 1/√2>, not < e^{-2}, e^{-2}>. The magnitude is correct.
  • qwen3.6:27b-mlx: pass — The solution correctly identifies the gradient, evaluates it at the specified point, and states that the maximum rate of change is the magnitude of the gradient in the direction of the gradient. The final answer matches the stated result.
Every verdict on record (4)
  • gpt-oss:20b: fail (misleading) 2026-10-06 — The direction of maximum increase should be the unit vector in the direction of the gradient, i.e. <1/√2, 1/√2>, not < e^{-2}, e^{-2}>. The magnitude is correct.
  • qwen3.6:27b-mlx: pass 2026-10-06 — The solution correctly identifies the gradient, evaluates it at the specified point, and states that the maximum rate of change is the magnitude of the gradient in the direction of the gradient. The final answer matches the stated result.
  • gpt-oss:20b: pass 2026-10-06
  • qwen3.6:27b-mlx: pass 2026-10-06 — The solution correctly identifies the gradient, evaluates it at the specified point, and applies the theorem that the maximum rate of change is the magnitude of the gradient in the direction of the gradient. The final answer matches the stated answer.

Proved: SymPy reduced the difference between the two sides to zero. Checked independently: a separate method, named above, confirmed it. Checked numerically: the two sides agree at every sampled point, which is evidence, not proof. Reviewed: a model or a person read it; that is all a sentence can have. Solution by generator:structured/max_rate_of_change, checked 2026-10-06 with SymPy 1.14.0.