When stochastic gradient descent is applied to the Airfoil Self-Noise dataset with a batch size of $$1$$ and a learning rate of $$0.005$$, the model parameters are updated $$1{,}500$$ times per epoch (once per example). Although the objective function value declines rapidly within the first epoch in terms of examples processed, the wall-clock time per epoch is approximately $$0.685$$ seconds—over $$30$$ times slower than batch gradient descent's $$0.020$$ seconds per epoch. This occurs because processing individual observations one at a time cannot leverage hardware vectorization and incurs higher per-update overhead, making each gradient computation less efficient despite the more frequent parameter updates.

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Batch gradient descent (batch size = $$N$$) produces low-noise gradient estimates and takes large, reliable steps toward the minimum. However, it may require considerable time per iteration and significant additional memory.

Stochastic gradient descent (batch size = $$1$$) is memory-efficient and well-suited for large datasets. However, it is extremely noisy because individual training examples may point in poor directions. SGD tends to oscillate and wander around the region of the minimum rather than converging directly to it.

Minibatch gradient descent (batch size between $$1$$ and $$N$$) offers a practical compromise. Although it does not guarantee monotonic progress toward the minimum, it tends to head more consistently in the right direction.

Experimentally, while SGD converges faster than batch GD in terms of the number of examples processed, it consumes more wall-clock time because computing the gradient example by example is computationally less efficient. Minibatch SGD balances convergence speed and computation efficiency: for instance, a batch size of $$100$$ can even outperform full-batch GD in runtime.

Batch vs Stochastic vs Mini-Batch Gradient Descent

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When batch gradient descent is applied to the Airfoil Self-Noise dataset by setting the minibatch size equal to the total number of training examples ($$1{,}500$$), the model parameters are updated only once per epoch. With a learning rate of $$1$$ over $$10$$ epochs, the loss converges to approximately $$0.247$$ at a speed of about $$0.020$$ seconds per epoch. However, progress is minimal: after roughly $$6$$ parameter updates, the loss curve plateaus and further improvement stalls. This demonstrates the fundamental limitation of full-batch gradient descent—each epoch provides only a single update, so achieving fine convergence requires many epochs despite each individual epoch being fast to compute.

Batch GD Slow Convergence on the Airfoil Dataset

SGD Slower Wall-Clock Time Despite Faster Per-Example Convergence

When minibatch stochastic gradient descent is applied to the Airfoil Self-Noise dataset with a batch size of $$100$$ and a learning rate of $$0.4$$, it requires approximately $$0.025$$ seconds per epoch—comparable to full-batch gradient descent—while converging substantially faster in terms of loss reduction. Reducing the batch size to $$10$$ (with learning rate $$0.05$$) increases the epoch time to about $$0.090$$ seconds because smaller batches are less efficient to execute on hardware, yet this is still faster than pure SGD. A final time-versus-loss comparison plotted on a logarithmic time axis across all four methods (full-batch GD, SGD, batch size $$100$$, and batch size $$10$$) confirms that although SGD converges faster than GD in terms of examples processed, it uses more wall-clock time to reach the same loss because per-example gradient computation is not as efficient. Minibatch SGD achieves the best overall trade-off: a batch size of $$100$$ can even outperform full-batch GD in total runtime by making $$15$$ parameter updates per epoch instead of just one, while retaining the computational benefits of vectorized operations.

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