Data Fundamentals (H) - Week 06 Quiz
1. I want to find the shape of an object, with constant surface area, that holds the least water. What is the objective function?
The shape of the object.
The surface area of the object.
None of the above.
The amount of water the object holds.
The colour of the surface.
2. A convex constraint is equivalent to a restriction to a portion of the parameter space:
within a torus of fixed radius.
defined by a collection of planes.
inside an axis-aligned box.
where the parameter vector has a fixed \(L_\infty\) norm.
where the minima are.
3. An objective function is nonconvex, iff:
It is incomputable.
It is partially differentiable.
It more than one minimum.
It has two maxima.
It is discontinuous.
4. The
feasible set
in an optimisation problem is:
the most distant configurations in the parameter space
the possible values of the objective function
a kind of metaheuristic
the best solutions to the problem
the possible configurations of the parameters
5. In an approximation problem, we'd often have a loss function of the form:
\(L(\theta) = \frac{1}{\theta}\)
\(L(\theta) = \frac{\theta}{f(\vec{x}-\vec{\theta})}\)
\(L(\theta) = \theta \vec{x}\)
\(L(\theta) = \|\theta - \vec{x}\|\)
\(L(\theta) = \|f(\vec{x};\theta)-y\|\)
6. The definition of an eigenvector is:
\(A\lambda = \vec{x}A\)
\(A\vec{x} = x\)
\(\lambda = \|\vec{x}\|_2\)
\(A^{-1}\vec{x} = A^{+}\lambda\)
\(A\vec{x} = \lambda x\)
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