Which of the following best describes the role of the gradient in gradient descent?

Question

BlackTom AI · Accepted Answer

We are asked to describe the role of the gradient in gradient descent, and we are given a single answer option to evaluate.
Option: 'It points in the direction of the maximum increase in the function value.'
First, consider what the gradient of a function represents: it points in the direction where the function increases most rapidly, i.e., the direction of steepest ascent. This is a fundamental property of gradients in multivariable calculus.
Next, apply this to gradient descent: the algorithm updates parameters by moving in the opposite direction of the gradient, i.e., by subtracting a step proportional to the gradient. This negative gradient step seeks to decrease the function value, climbing down the hill rather than climbing up the hill.
So, while the statement correctly describes the gradient itself (the direction of maximum increase), it does not describe the action taken during gradient descent, which is to move in the opposite direction to reduce the function value. This means the option is technically correct about the gradient’s direction, but it does not capture the action taken in gradient descent, which is a key part of the method’s mechanism.
In summary, the gradient does point toward the maximum increase, but gradient descent uses the negative of that direction to minimize the function. If the question focuses strictly on the gradient’s inherent direction, the statement is correct; if it focuses on the update rule in gradient descent, the statement is incomplete because the descent step moves opposite to that direction.

COMP90049_2025_SM2 Lecture 8 Gradient Descent & Logistic Regression Practice Quiz

Which of the following best describes the role of the gradient in gradient descent?

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