<eventitemdate="2010-10-26"time="04:30 PM"room="MC4040"title="Analysis of randomized algorithms via the probabilistic method">

<short><p>In this talk, we will give a few examples that illustrate the basic method and show how it can be used to prove the existence of objects with desirable combinatorial properties as well as produce them in expected polynomial time via randomized algorithms. Our main goal will be to present a very slick proof from 1995 due to Spencer on the performance of a randomized greedy algorithm for a set-packing problem. Spencer, for seemingly no reason, introduces a time variable into his greedy algorithm and treats set-packing as a Poisson process. Then, like magic, he is able to show that his greedy algorithm is very likely to produce a good result using basic properties of expected value.

</p></short>

<abstract><p>The probabilistic method is an extremely powerful tool in combinatorics that can be

used to prove many surprising results. The idea is the following: to prove that an

object with a certain property exists, we define a distribution of possible objects

and use show that, among objects in the distribution, the property holds with

non-zero probability. The key is that by using the tools and techniques of

probability theory, we can vastly simplify proofs that would otherwise require very

complicated combinatorial arguments.

</p><p>As a technique, the probabilistic method developed rapidly during the latter half of

the 20th century due to the efforts of mathematicians like Paul Erdős and increasing

interest in the role of randomness in theoretical computer science. In essence, the

probabilistic method allows us to determine how good a randomized algorithm's output

is likely to be. Possibly applications range from graph property testing to

computational geometry, circuit complexity theory, game theory, and even statistical

physics.

</p><p>In this talk, we will give a few examples that illustrate the basic method and show

how it can be used to prove the existence of objects with desirable combinatorial

properties as well as produce them in expected polynomial time via randomized

algorithms. Our main goal will be to present a very slick proof from 1995 due to

Spencer on the performance of a randomized greedy algorithm for a set-packing

problem. Spencer, for seemingly no reason, introduces a time variable into his

greedy algorithm and treats set-packing as a Poisson process. Then, like magic,

he is able to show that his greedy algorithm is very likely to produce a good

result using basic properties of expected value.

</p><p>Properties of Poisson and Binomial distributions will be applied, but I'll remind

everyone of the needed background for the benefit of those who might be a bit rusty.

Stat 230 will be more than enough. Big O notation will be used, but not excessively.

</p></abstract>

</eventitem>

<eventitemdate="2010-10-19"time="04:30 PM"room="RCH 306"title="Machine learning vs human learning - will scientists become obsolete?">