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