Data Fundamentals (H) - Week 07 Quiz
1. Simulated annealing uses what metaheuristic to help avoid getting trapped in local minima?
Crossover rules.
Hill climbing.
A population of solutions.
Randomised restart.
A temperature schedule.
2. A
hyperparameter
of an optimisation algorithm is:
The determinant of the Hessian.
A value that affects how a solution is searched for.
A measure of how good a solution is.
A direction in hyperspace.
A value that is used to impose constraints on the solution.
3. First-order optimisation requires that objective functions be:
\(C^1\) continuous
monotonic
disconcerting
invertible
one-dimensional
4. The gradient vector \(\nabla L(\theta)\) is a vector which, at any given point \(\theta\) will:
have \(L_2\) norm 1
be equal to \(\theta\)
point in the direction of steepest descent
point towards the global minimum of \(L(\theta)\)
be zero
5. Finite differences is not an effective approach to apply first-order optimisation because:
of numerical roundoff issues.
all of the above
none of the above
the effect of measurement noise
the curse of dimensionality
6. Ant colony optimisation applies which two metaheuristics to improve random local search?
temperature and memory
thants
gradient descent and crossover
random restart and hyperdynamics
memory and population
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