## Lecture 25: Online Steiner Tree

The algorithm for probabilistically embedding metric spaces into trees has numerous theoretical applications. It is a key tool in the design of many approximation algorithms and online algorithms. Today we will illustrate the usefulness of these trees in designing an … Continue reading

## Lecture 24: Probabilistic approximation of metrics by trees

1. Probabilistic Approximation of Metrics For many optimization problems, the input data involves some notion of distance, which we formalize as a metric. But unfortunately many optimization problems can be quite difficult to solve in an arbitrary metric. In this … Continue reading

## Lecture 23: Random partitions of metric spaces (continued)

We continue our theorem from last time on random partitions of metric spaces 1. Review of Previous Lecture Define the partial Harmonic sum . Let be the ball of radius around . Theorem 1 Let be a metric with . … Continue reading

## Lecture 22: Random partitions of metric spaces

For many problems in computer science, there is a natural notion of “distance”. For example, perhaps the input data consists of real vectors for which it makes sense to measure their distance via the usual Euclidean distance. But in many … Continue reading

## Lecture 21: Property Testing

1. Overview of Property Testing Property testing is a research area in theoretical computer science that has seen a lot of activity over the past 15 years or so. A one-sentence description of this area’s goal is: design algorithms that, … Continue reading