Python代写-CSE 251A
CSE 251A: Machine learning Winter 2021
Programming project 2 — Coordinate descent
In this project we consider a standard unconstrained optimization problem:
where L(·) is some cost function and w ∈ Rd. In class, we looked at several approaches to solving such
problems—such as gradient descent and stochastic gradient descent—under differentiability conditions on
L(w). We will now look at a different, and in many ways simpler, approach:
• Initialize w somehow.
• Repeat: pick a coordinate i ∈ {1, 2, . . . , d}, and update the value of wi so as to reduce the loss.
Two questions need to be answered in order to fully specify the updates:
(a) Which coordinate to choose?
(b) How to set the new value of wi?
Give answers to these questions, and thereby flesh out a coordinate descent method. For (a), your solution
should do something more adaptive than merely picking a coordinate at random.
Then implement and test your algorithm on a logistic regression problem. First download the wine data
set that we have frequented alluded to in class:
This contains 178 data points in 13 dimensions, with 3 classes. Just keep the first two classes (with 59 and
71 points, respectively) so as to create a binary problem.
What to turn in
On the due date, upload (to gradescope) a typewritten report containing the following elements (each
labeled clearly).
1. A short, high-level description of your coordinate descent method.
In particular, you should give a concise description of how you solve problems (a) and (b) above. Do you
need the function L(·) to be differentiable (and maybe even have continuous second-order derivatives),
or does it work with any cost function?
2. Convergence.
Under what conditions do you think your method converges to the optimal loss? There’s no need to
prove anything: just give a few sentences of brief explanation.
CSE 251A Programming project 2 — Coordinate descent Winter 2021
3. Experimental results.
(Remember that all training must take place on just classes 1 and 2.)
• Begin by running a standard logistic regression solver (e.g., from scikit-learn) on the training
set. It should not be regularized: if the solver forces you to do this, just set the regularization
constant suitably to make it irrelevant. Make note of the final loss L∗.
• Then, implement your coordinate descent method and run it on this data.
• Finally, compare to a method that chooses coordinates i uniformly at random and then updates
wi using your method (we’ll call this “random-feature coordinate descent”).
• Produce a clearly-labeled graph that shows how the loss of your algorithm’s current iterate—
that is, L(wt)—decreases with t; it should asymptote to L
∗. On the same graph, show the
corresponding curve for random-feature coordinate descent.
4. Critical evaluation.
Do you think there is scope for further improvement in your coordinate descent scheme in (1); if so,
5. (Optional) Sparse coordinate descent.
Now, suppose we want a k-sparse solution w: that is, one that has at most k nonzero entries.
• Propose a modified version of your method for this task. Assume k is part of the input, along
with the data.
• Do you think this method always find the best k-sparse solution when L(·) is convex?
• Try this out on the wine data. Make a table of loss values obtained for a few selected values of k.