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Previously, `MetricAlignmentOptimizer.optimize_weights()` used the `cvxpy` built-in `cvxpy.entr(x)` entropy penalty. What I didn't realise until I ran the optimizer on real data, is that this function is actually concave. Therefore, it is possible, that the overall optimization problem is not convex anymore, messing up our convex solver. This adapts the entropy penalty to be a basic L2 penalty, which is convex. Fixes #16
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Previously,
MetricAlignmentOptimizer.optimize_weights()used thecvxpybuilt-incvxpy.entr(x)entropy penalty. What I didn't realise until I ran the optimizer on real data, is that this function is actually concave. Therefore, it is possible, that the overall optimization problem is not convex anymore, messing up our convex solver.This adapts the entropy penalty to be a basic L2 penalty, which is convex.
Fixes #16