How does random initialization help in gradient descent methods?

Question

BlackTom AI · Accepted Answer

When examining how random initialization influences gradient descent, we must consider the landscape of the objective function and optimization dynamics.
Option 1: "It ensures that the gradient descent always finds the global minimum." This is not correct in general because random initialization cannot guarantee convergence to the global minimum for non-convex problems; multiple local minima or saddle points can trap the process, and randomness only affects starting point, not the global guarantee.
Option 2: "It helps to avoid getting stuck in saddle points." This is a nuanced and commonly cited benefit. Random initializations can help gradient-based methods explore diverse regions of the parameter space, and certain stochastic or noisy dynamics can help escape saddle points or flat regions, making it less likely to become trapped in these problematic areas compared to a fixed, potentially adversarial starting point.
Option 3: "It guarantees faster convergence." This statement is overstated. Random initialization may sometimes lead to more favorable starting regions, but it does not guarantee faster convergence; in some cases it could even slow convergence if the initial point is poor or if the optimization landscape is deceptive.
In summary, the second option aligns with the practical understanding that randomness in initialization can reduce the risk of stagnation at saddle points by providing diverse starting locations, whereas the other claims overstate guarantees that do not hold for general non-convex optimization and do not reflect the core role of initialization randomness.

Learning AI Through Visualization 4 Module 3 Quiz

How does random initialization help in gradient descent methods?

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