 SGDR

SGDR means stochastic gradient descent with warm restarts. Its learning rate schedule repeatedly lowers the learning rate along a cosine curve, then raises it to begin another cycle while retaining the current parameters. Ilya Loshchilov and Frank Hutter's method uses this warm restart to continue an existing optimization trajectory.^[ref]

In cycle $i$, let $s$ be elapsed training time since the last restart and $T_i>0$ the cycle duration, measured in the same units. The cosine learning rate schedule isIt starts at ${\eta }_{\max ,i}$, reaches the midpoint of the two bounds at $s=T_i/2$, and reaches ${\eta }_{\min ,i}$ at the cycle endpoint. The original implementation updates the elapsed training epoch count within each mini-batch, allowing fractional epoch progress.^[ref]

For an original schedule, choose bounds 0 and 0.1, initial duration $T_0=4$ epochs and duration multiplier 2. The successive cycle lengths are 4, 8 and 16 epochs, with restart boundaries at total elapsed times 4, 12 and 28. In the first continuous cycle, the rates at $s=0,2,4$ are respectively $0.1,0.05,0$.

A discrete convention must specify which update uses the endpoint. For example, sampling phases $s=0,1,2,3$ in the first cycle and restarting before the next step gives rates approximately $0.1,0.08536,0.05,0.01464$, followed by $0.1$. The zero endpoint in the continuous curve is then a limit, rather than an additional zero-rate optimizer step.

Retaining parameters also has a concrete meaning. In an original deterministic gradient descent example with $f(\theta )={\theta }^2/2$ and current $\theta =0.5$, resetting only the schedule leaves $\theta =0.5$. The next step at learning rate0.1 gives0.45; a step at0.01 would give0.495. The restart changes the next movement rather than drawing a new initial parameter. Handling a momentum buffer is a further state convention; restarting the schedule and discarding all optimizer state are different operations. Record cycle phase, durations and rate bounds with an optimization checkpoint.