Relating Checkpoint Update Probabilities to Momentum Parameters in Single-Loop Variance Reduction Methods

We propose a single-loop variance-reduced acceleration framework, which relates checkpoint update probabilities to momentum parameters, for solving the composite general convex problem where the smooth part has the finite-sum structure. Under the proposed framework, the growth rate of the momentum parameter is further altered, creating a novel continuous trade-off between acceleration and variance reduction, controlled by the key parameter $\alpha\in [0,1]$. A series of novel complexity is obtained, and some complexity of distinct known methods are rediscovered under the unified framework. When the mini-batch size is restricted due to the massive scale of the problem or the computational resource shortage, near-optimal complexity can still be achieved by choosing suitable $\alpha$ for any prefixed target accuracy. Analysis shows that although the considered gradient oracle is exact, acceleration comes with implicit price of heavier variance reduction, hence the obtained optimal $\alpha$ not necessarily corresponds to the largest allowable acceleration strength. Without prefixing the target accuracy, the proposed method achieves the near-optimal complexity $\tilde{\mathcal{O}}(n+\sqrt{n}/\sqrt{\epsilon})$ to obtain an $\epsilon$-accurate solution under standard assumptions ($n$ is the number of components of the finite-sum), significantly improves upon previous best complexity $\mathcal{O}(n+n/\sqrt{\epsilon})$ of single-loop variance reduction methods, which does not exceed the complexity of the deterministic method FISTA. Numerical experiments demonstrate the efficiency of the proposed method and validate other theoretical findings.