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