∫Calc Practice

Derivative of \( \displaystyle - x \ln{\left(x - 1 \right)} + x + \ln{\left(x - 1 \right)} \)

Problem 2.1003 · hard Beautiful

Differentiate \( \displaystyle f(x) = - x \ln{\left(x - 1 \right)} + x + \ln{\left(x - 1 \right)} \).
  1. \[ \frac{d}{d x} \left(- x \ln{\left(x - 1 \right)} + x + \ln{\left(x - 1 \right)}\right) \]
    derivativeStart with the derivative of the function.✓ Proved
  2. \[ = \frac{d}{d x} x + \frac{d}{d x} \left(- x \ln{\left(x - 1 \right)}\right) + \frac{d}{d x} \ln{\left(x - 1 \right)} \]
    sumApply the sum rule.✓ Proved
  3. \[ = \frac{d}{d x} \left(- x \ln{\left(x - 1 \right)}\right) + \frac{d}{d x} \ln{\left(x - 1 \right)} + 1 \]
    constantDifferentiate the term x.✓ Proved
  4. \[ = \frac{d}{d x} \left(- x \ln{\left(x - 1 \right)}\right) + 1 + \frac{1}{x - 1} \]
    logarithmicDifferentiate the log term.✓ Proved
  5. \[ = - x \frac{d}{d x} \ln{\left(x - 1 \right)} + \ln{\left(x - 1 \right)} \frac{d}{d x} \left(- x\right) + 1 + \frac{1}{x - 1} \]
    productApply the product rule to the first term.✓ Proved
  6. \[ = - \frac{x}{x - 1} - \ln{\left(x - 1 \right)} + 1 + \frac{1}{x - 1} \]
    derivative constant-multiple algebraDifferentiate the components of the product. Simplify the coefficient -1. Group the fractions together.✓ Proved
  7. \[ = \frac{1 - x}{x - 1} - \ln{\left(x - 1 \right)} + 1 \]
    algebraCombine the fractions.✓ Proved
  8. \[ = - \ln{\left(x - 1 \right)} \]
    algebra algebra simplifyRewrite 1 with a common denominator. Simplify the numerator. Final simplification.✓ Proved
Answer \( - \ln{\left(x - 1 \right)} \)

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 1.14.0line 1 is the problem as stated
2✓ Provedsympy 1.14.0simplify(a - b) reduced to 0
log is undefined for non-positive arguments
3✓ Provedsympy 1.14.0simplify(a - b) reduced to 0
log is undefined for non-positive arguments
4✓ Provedsympy 1.14.0simplify(a - b) reduced to 0
log is undefined for non-positive arguments
undefined where x - 1 = 0
5✓ Provedsympy 1.14.0simplify(a - b) reduced to 0
log is undefined for non-positive arguments
undefined where x - 1 = 0
6✓ Provedsympy 1.14.0simplify(a - b) reduced to 0
log is undefined for non-positive arguments
undefined where x - 1 = 0
7✓ Provedsympy 1.14.0simplify(a - b) reduced to 0
log is undefined for non-positive arguments
undefined where x - 1 = 0
8✓ Provedsympy 1.14.0simplify(a - b) reduced to 0
log is undefined for non-positive arguments
undefined where x - 1 = 0
9✓ Provedsympy 1.14.0simplify(a - b) reduced to 0
log is undefined for non-positive arguments
undefined where x - 1 = 0
10✓ Provedsympy 1.14.0simplify(a - b) reduced to 0
log is undefined for non-positive arguments
undefined where x - 1 = 0
11✓ Provedsympy 1.14.0simplify(a - b) reduced to 0
log is undefined for non-positive arguments
12✓ Provedsympy 1.14.0simplify(a - b) reduced to 0
log is undefined for non-positive arguments
answer✓ Provedsympy 1.14.0final line against the stated answer: simplify(a - b) reduced to 0
log is undefined for non-positive arguments
answer, a second way✓ Provedsympy 1.14.0SymPy differentiated f directly and got the stated answer

Reviewers

  • gpt-oss:20b: pass
  • qwen3.6:27b-mlx: pass — The solution correctly applies differentiation rules and algebraic simplifications. Each step adheres to the single-change constraint and uses valid labels from the fixed vocabulary.
Every verdict on record (4)
  • qwen3.6:27b-mlx: pass 2026-09-27 — The solution correctly applies differentiation rules and algebraic simplifications. Each step adheres to the single-change constraint and uses valid labels from the fixed vocabulary.
  • gpt-oss:20b: pass 2026-09-27
  • qwen3.6:27b-mlx: pass 2026-09-27 — The solution correctly applies differentiation rules and algebraic simplifications in single-step increments. All labels are appropriate for the operations performed.
  • gpt-oss:20b: fail (error) 2026-09-27 — Step 6 applies two derivative rules in one step (both Derivative(-x) and Derivative(log(x-1))) while only one rule may be applied per step.

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 gemma4:26b, checked 2026-09-27 with SymPy 1.14.0.