Which of the following is NOT true about the steepest descent method?

Question

BlackTom AI · Accepted Answer

The task presents a question about the steepest descent method and lists an answer, but the provided answer options array is empty, which means we do not have the full set of statements to analyze.

First, acknowledge the missing options: without the list of statements to evaluate, we cannot systematically assess each choice as true or false. In a complete multiple-choice item, we would compare every option to standard properties of steepest descent.

Nevertheless, we can still reason about the core concept: steepest descent (gradient descent) relies on the gradient of the objective function to determine the direction of the steepest decrease in value. Therefore, a statement claiming that there is no need to compute any derivative is generally incorrect, because:
- The method uses the gradient (first derivative) to identify the descent direction at the current point.
- If derivatives were not required, we would not have a principled way to move toward lower function values.
- In many implementations, the gradient is computed at each iteration, and a line search or step size is chosen to ensure sufficient decrease, which again depends on derivative information.

If we hypothesize what other statements might typically appear in such a question, they could include ideas like:
- The method requires knowledge of the gradient (true for standard steepest descent).
- A line search is sometimes used to select an appropriate step size (true in many variants).
- The method guarantees convergence for strictly convex quadratic functions under appropriate conditions (true under certain assumptions, but not universally guaranteed for all functions).

Because the provided option asserts that there is no need to compute any derivative, and given the fundamental role of the gradient in steepest descent, this statement would be incorrect in a standard interpretation.

In the absence of the other options, this explanation emphasizes why a derivative-free claim conflicts with the core mechanics of the steepest descent algorithm, which fundamentally relies on gradient information to determine descent direction and step size optimization.

BN5206 Re-opened Canvas questions [NON-GRADED, FOR PRACTICE]

Which of the following is NOT true about the steepest descent method?

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