∫Calc Practice

Implicit differentiation with partial derivatives

Problem 10.516 · medium

The equation \( \displaystyle x^{2} + x z + y^{2} z = 7 \) defines \( \displaystyle z \) as a function of \( \displaystyle x \) and \( \displaystyle y \) near \( \displaystyle (2, -1, 1) \). Find \( \displaystyle \frac{\partial z}{\partial x} \) there.
  1. \[ 7 \]
    The point is on the surface.✓ Proved
  2. \[ \left[\begin{matrix}\frac{\partial}{\partial x} \left(x^{2} + x z + y^{2} z\right)\\\frac{\partial}{\partial z} \left(x^{2} + x z + y^{2} z\right)\end{matrix}\right] = \left[\begin{matrix}2 x + z\\x + y^{2}\end{matrix}\right] \]
    F_x and F_z.✓ Proved
  3. \[ - \frac{5}{3} \]
    ∂z/∂x = −F_x/F_z.✓ Proved
Answer \( - \frac{5}{3} \)

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 1.3.0solved the equation numerically near the point and differenced

Reviewers

  • gpt-oss:20b: pass
  • qwen3.6:27b-mlx: pass — The solution correctly applies the implicit function theorem formula ∂z/∂x = -F_x/F_z and evaluates the partial derivatives at the given point.
Every verdict on record (4)
  • gpt-oss:20b: pass 2026-10-12
  • qwen3.6:27b-mlx: pass 2026-10-12 — The solution correctly applies the implicit function theorem formula ∂z/∂x = -F_x/F_z and evaluates the partial derivatives at the given point.
  • gpt-oss:20b: pass 2026-10-12
  • qwen3.6:27b-mlx: fail (error) 2026-10-12 — The solution fails to substitute the point (2, -1, 1) into the partial derivatives before computing the ratio. It presents the final numerical answer without showing the evaluation step, making the derivation incomplete and potentially confusing for a student.

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/implicit_partials, checked 2026-10-12 with SymPy 1.14.0.