Lecture 11 - Stochastic Optimisation Flashcards

1
Q

What are stochastic optimization methods?

A

Methods that use random variables as part of the optimization.

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2
Q

What 2 situations give rise to stochastic optimization?

A

Optimization of Stochastic functions - objective functions which contain random noise.
Optimization algorithms with random search methods - search for the optimum using na algorithm that contains an element of randomness.

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3
Q

What are the consequences of stochasticity in objective functions?

A

Evaluation of objective function is no longer accurate

Evaluation of gradient of objective function no longer accurate

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4
Q

Why are stochastic search algorithms needed?

A

Can inject randomness into the search process which can help speed convergence and improve robustness in cases where the global optimum is hard to find.

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