LR increase by epoch is usually only done as “warmup” for optimizers that collect gradient stats, like adam.  And I’m not sure if LR decrease is warranted with that graph. So, in this specific case, overall LR increase can maybe reach same training loss in less epochs, but adding a LR decay scheduler may be advisable (depends on optimizer too).
It is hard to say how to schedule LR optimally - some people even do hyperoptimization runs to find that.
But my point was, your validation graph is fine if the slowdown just reflects slowdown on a training graph. As for reasoning about overfit, you kinda have to “smooth” plot points one way or the other, as the volatility is inherent.