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Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform

ICLR 2020 · Optimization › Optimizers · approach: Second-order and structured preconditioning · 27 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use Cayley SGD with momentum instead of SGDWRN-28-10 · CIFAR10classification error (%): 3.89 -> 3.66Helped
use Cayley ADAM instead of ADAMWRN-28-10 · CIFAR100classification error (%): 18.52 -> 18.10Helped
use the iterative Cayley method instead of the closed-form Cayley transformCayley SGD and Cayley ADAM · WRN-28-10, CIFAR10training time per epoch (s): 1071.5 -> 218.7; 1071.5 -> 224.4Helped
add momentum to the closed-form Cayley optimization (ablation)WRN-28-10 · CIFAR10classification error (%): 3.80 -> 3.66Helped
add momentum to the closed-form Cayley optimization (ablation)WRN-28-10 · CIFAR100classification error (%): 18.68 -> 18.26Helped
use iterative Cayley SGD instead of the closed-form Cayley transformFull-uRNN, hidden size 116 · Pixel-by-pixel MNISTtime per iteration (s): 2.10 -> 1.42Helped
use iterative Cayley ADAM instead of the closed-form Cayley transformFull-uRNN, hidden size 512 · Pixel-by-pixel MNISTaccuracy (%): 96.9 -> 96.9No clear effect
increase iterative Cayley steps from s=0 to s=2 (ablation)Full-uRNN, hidden size 116 · unitarity checkunitarity error: 3.231e-3 -> 7.384e-6Helped
increase iterative Cayley steps from s=0 to s=2 (ablation)Full-uRNN, hidden size 512 · unitarity checkunitarity error: 6.787e-3 -> 2.562e-5Helped
use s=2 iterative Cayley instead of the closed-form transform (ablation)Full-uRNN, hidden size 116 · unitarity checkunitarity error: 8.273e-5 -> 7.384e-6Helped

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