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