∫Calc Practice

Gradient and directional derivatives

Problem 10.153 · medium Video

Find the directional derivative of \( \displaystyle f(x, y) = x^{2} + 3 x y + y^{2} \) at \( \displaystyle (2, 1) \) in the direction of \( \displaystyle \langle 3, 4 \rangle \).
  1. \[ \left[\begin{matrix}\frac{\partial}{\partial x} \left(x^{2} + 3 x y + y^{2}\right)\\\frac{\partial}{\partial y} \left(x^{2} + 3 x y + y^{2}\right)\end{matrix}\right] = \left[\begin{matrix}2 x + 3 y\\3 x + 2 y\end{matrix}\right] \]
    ∇f.✓ Proved
  2. \[ \left[\begin{matrix}7\\8\end{matrix}\right] \]
    ∇f at the point.Reviewed
  3. \[ \left[\begin{matrix}\frac{3}{5}\\\frac{4}{5}\end{matrix}\right] \]
    The unit direction u.Reviewed
  4. \[ \frac{53}{5} \]
    D_u f = ∇f · u.Reviewed
Answer \( \frac{53}{5} \)
A contour map of f(x, y) = x² + 3x·y + y²: each curve joins points where f has the same value. At (2, 1) the gradient arrow points straight uphill, across the contours, and the direction of travel is drawn beside it.

✓ Nihil obstat Lines: 1 proved, 3 reviewed. The answer was also checked a second way, without looking at the solution. Reviewers found nothing wrong with the explanation. How we verify

The full receipt
LineStatusChecked byDetail
1✓ Provedsympy 1.14.0simplify(lhs - rhs) reduced to 0
2Reviewedreviewers: gpt-oss:20b, qwen3.6:27b-mlxboth sides are the same expression: the line states a value, and the working that produced it is not on the line
3Reviewedreviewers: gpt-oss:20b, qwen3.6:27b-mlxboth sides are the same expression: the line states a value, and the working that produced it is not on the line
4Reviewedreviewers: gpt-oss:20b, qwen3.6:27b-mlxboth sides are the same expression: the line states a value, and the working that produced it is not on the line
answer, a second way✓ Checked independentlysympy 1.14.0 + mpmath 1.3.0f differenced along the unit direction agrees

Reviewers

  • gpt-oss:20b: pass
  • qwen3.6:27b-mlx: pass
Every verdict on record (6)
  • qwen3.6:27b-mlx: pass 2026-09-27
  • gpt-oss:20b: pass 2026-09-27
  • qwen3.6:27b-mlx: pass 2026-09-26
  • gpt-oss:20b: pass 2026-09-26
  • qwen3.6:27b-mlx: pass 2026-09-26 — The solution correctly computes the gradient, normalizes the direction vector, and calculates the dot product. The steps are logically sound and the final answer is correct.
  • gpt-oss:20b: pass 2026-09-26

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/gradient_directional, checked 2026-10-13 with SymPy 1.14.0.