How do different cost functions affect convergence speed?

Cost function geometry directly affects gradient smoothness and convergence rate.

Smooth, convex cost functions like MSE allow fast convergence, while sharp or poorly conditioned losses slow optimization. Loss curvature determines effective step sizes and conditioning number, which directly impacts gradient descent efficiency.