# Stanford EE364A Convex Optimization I Stephen Boyd I 2023 I Lecture 11

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## Binary Hypothesis Testing

• The goal is to determine which of two distributions generated an observed sample.
• A randomized detector is introduced as a policy for attributing observations to distributions.
• Scalarizing the problem simplifies the solution.
• Minimizing the total number of errors leads to a simple solution based on maximum likelihood.
• Minimizing the maximum of false positive and false negative errors results in a non-deterministic detector.

## Experiment Design

• The concept of experiment design involves linear measurements and a measurement vector.
• The measurements are normalized to have the same standard deviation.
• The goal is to choose the best subset of experiments to obtain the best estimate of a parameter.
• The optimality criterion is based on the size of the confidence ellipsoid.
• A simple experiment design method is to choose the experiment with the highest signal-to-noise ratio.
• The Gaussian model allows for the experiment design problem to be formulated as an integer programming problem.
• A relaxation technique for solving experiment design problems is introduced.
• The speaker suggests using a probabilistic approach to determine which experiment to perform.
• The rounded solution may not minimize the confidence ellipsoid volume but is close to the optimal solution.
• The assumption of M (the number of experiments) being much larger than P (the number of options) is made.
• The speaker introduces the concept of D-optimal experiment design.

## Optimization Problems Involving Polyhedra and Ellipsoids

• The goal is to find the minimum volume ellipsoid that covers the given experiments.
• The experiments with higher signal-to-noise ratio (SNR) are more desirable and should be chosen.
• The optimal experiment design chooses two experiments that are closest to orthogonal to each other.
• Adding a budget constraint allows for cost-effective experiment design.
• Complex constraints can be incorporated into the convex optimization problem.
• The volume of the ellipsoid is inversely proportional to the square root of the determinant of W.
• Determining if two polyhedra intersect is a convex problem.
• Finding the minimum distance between two polyhedra is a convex optimization problem.

## Minimum Volume Ellipsoid

• This can be formulated as a convex optimization problem.
• One application is in outlier detection.
• A method called "ellipsoidal peeling" can be used to iteratively remove outliers and find the minimum volume ellipsoid.
• Finding the minimum volume ellipsoid that covers a polyhedron given an inequality form is NP-hard.
• The dual problem of finding the maximum volume ellipsoid that fits inside a polyhedron is tractable if the polyhedron is described by inequalities but NP-hard if it's described by its vertices.
• The Löwner-John ellipsoid is the minimum volume ellipsoid that covers a convex bounded set with non-empty interior.
• Ellipsoids are universal approximators of convex sets within a factor of √n.
• Any norm on ℝ^n can be approximated with a quadratic norm with error no more than the fourth root of n.