Research map / Optimization / Nonconvex and smooth optimization
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add Nesterov momentum to SVRCD | SVRCD · Quadratic minimization with P = I | convergence | Helped |
| Use importance sampling instead of uniform sampling | ASVRCD and SVRCD · Quadratic minimization with P = I | convergence | Helped |
| Reduce the rank of the projection matrix P | ASVRCD and SVRCD · Quadratic minimization with varying P | convergence | Helped |
| Exploit an affine-subspace indicator in the regularizer through the proximal operator | SEGA and SVRCD · Strongly convex composite optimization | iteration complexity: d -> 1 | Helped |
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