Research map / Optimization / Nonconvex and smooth optimization
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GPDA with sufficiently large β | GPDA · Random matrix Q, N=20, M=5 | convergence behavior | Helped |
| Use a small β | GPDA · Random matrix Q, N=20, M=5 | convergence behavior | Hurt |
| Initialize at the strict saddle and run GPDA | GPDA · 2-D constrained example | saddle escape | Helped |
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