Research map / Optimization / Distributed and compressed
Communication-Efficient Distributed Optimization with Quantized Preconditioners
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use quantized preconditioned methods instead of vanilla methods | Distributed regression optimization · Standard regression datasets | total communication complexity: over 3 × | Helped |
| Add quantized preconditioning to gradient descent | Least-squares regression on cpusmall_scale · cpusmall_scale | convergence | Helped |
| Quantize the Hessian in Q-Newton | Logistic regression · phishing | convergence: 4 bits per coordinate | Helped |
| Use QPGD-GLM instead of the other tested methods | Logistic regression · german_numer | convergence | Mixed |
| Quantize preconditioners while retaining quantized gradient updates | Least-squares regression · cpusmall_scale | solution quality | No clear effect |
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