Data Fundamentals (H) - Week 07 Quiz
1. Simulated annealing uses what metaheuristic to help avoid getting trapped in local minima?
Hill climbing.
Randomised restart.
A population of solutions.
Crossover rules.
A temperature schedule.
2. A
hyperparameter
of an optimisation algorithm is:
A measure of how good a solution is.
A direction in hyperspace.
The determinant of the Hessian.
A value that is used to impose constraints on the solution.
A value that affects how a solution is searched for.
3. First-order optimisation requires that objective functions be:
one-dimensional
invertible
disconcerting
monotonic
\(C^1\) continuous
4. The gradient vector \(\nabla L(\theta)\) is a vector which, at any given point \(\theta\) will:
be zero
be equal to \(\theta\)
point in the direction of steepest descent
have \(L_2\) norm 1
point towards the global minimum of \(L(\theta)\)
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 curse of dimensionality
the effect of measurement noise
6. Ant colony optimisation applies which two metaheuristics to improve random local search?
temperature and memory
gradient descent and crossover
memory and population
random restart and hyperdynamics
thants
Submit Quiz