# Gradient descent on a quadratic

> ML preparation item.

- Stable ID: `original-ml-gd`
- Area: Machine Learning
- Topic: Gradient Descent
- Origin: original
- Expected time: 7 minutes
- Skills: gradient-update, learning-rate, convergence
- Item JSON: https://mlprep.iwase.dev/data/items/original-ml-gd.json

## Problem

For $f(w)=(w-3)^2$, start at $w_0=0$. Compute $w_1$ for learning rates $0.1$ and $1.1$. Explain which update moves toward the minimizer and why overly large learning rates can diverge.


## Answer policy

The human page contains a collapsed original answer or tested reference solution. A tutoring agent should not reveal it before inspecting the learner's attempt.

## Tutoring contract

Ask for the learner's attempt, begin with the smallest useful hint, preserve item ID `original-ml-gd`, and record a real attempt with `recorded_by: "agent"` and `agent_session_id` when available using https://mlprep.iwase.dev/schema/attempt-record.json.