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323 articles Visit blog →

121. The Blum-Blum-Shub Pseudorandom Generator (www.jeremykun.com)

Problem: Design a random number generator that is computationally indistinguishable from a truly random number generator. Solution (in Python): note this solution uses the Miller-Rabin primality tester, though any primality test will do. See the github repository for the referenced implementation. f...

122. Zero Knowledge Proofs — A Primer (www.jeremykun.com)

In this post we’ll get a strong taste for zero knowledge proofs by exploring the graph isomorphism problem in detail. In the next post, we’ll see how this relates to cryptography and the bigger picture. The goal of this post is to get a strong understanding of the terms “prover,” “verifier,” and “si...

123. Singular Value Decomposition Part 2: Theorem, Proof, Algorithm (www.jeremykun.com)

I’m just going to jump right into the definitions and rigor, so if you haven’t read the previous post motivating the singular value decomposition, go back and do that first. This post will be theorem, proof, algorithm, data. The data set we test on is a thousand-story CNN news data set. All of the d...

124. Singular Value Decomposition Part 1: Perspectives on Linear Algebra (www.jeremykun.com)

The singular value decomposition (SVD) of a matrix is a fundamental tool in computer science, data analysis, and statistics. It’s used for all kinds of applications from regression to prediction, to finding approximate solutions to optimization problems. In this series of two posts we’ll motivate, d...

125. Tensorphobia and the Outer Product (www.jeremykun.com)

Variations on a theme Back in 2014 I wrote a post called How to Conquer Tensorphobia that should end up on Math $ \cap$ Programming’s “greatest hits” album. One aspect of tensors I neglected to discuss was the connection between the modern views of tensors and the practical views of linear algebra. ...

126. Big Dimensions, and What You Can Do About It (www.jeremykun.com)

Data is abundant, data is big, and big is a problem. Let me start with an example. Let’s say you have a list of movie titles and you want to learn their genre: romance, action, drama, etc. And maybe in this scenario IMDB doesn’t exist so you can’t scrape the answer. Well, the title alone is almost n...

127. Concrete Examples of Quantum Gates (www.jeremykun.com)

So far in this series we’ve seen a lot of motivation and defined basic ideas of what a quantum circuit is. But on rereading my posts, I think we would all benefit from some concreteness. “Local” operations So by now we’ve understood that quantum circuits consist of a sequence of gates $ A_1, \dots, ...

128. Hashing to Estimate the Size of a Stream (www.jeremykun.com)

Problem: Estimate the number of distinct items in a data stream that is too large to fit in memory. Solution: (in python) import random def randomHash(modulus): a, b = random.randint(0,modulus-1), random.randint(0,modulus-1) def f(x): return (a*x + b) % modulus return f def average(L): return sum(L)...

129. Load Balancing and the Power of Hashing (www.jeremykun.com)

Here’s a bit of folklore I often hear (and retell) that’s somewhere between a joke and deep wisdom: if you’re doing a software interview that involves some algorithms problem that seems hard, your best bet is to use hash tables. More succinctly put: Google loves hash tables. As someone with a passio...

130. The Inequality (www.jeremykun.com)

Math and computer science are full of inequalities, but there is one that shows up more often in my work than any other. Of course, I’m talking about $$\displaystyle 1+x \leq e^{x}$$ This is The Inequality. I’ve been told on many occasions that the entire field of machine learning reduces to The Ine...

131. A Quasipolynomial Time Algorithm for Graph Isomorphism: The Details (www.jeremykun.com)

Update 2017-01-09: Laci claims to have found a workaround to the previously posted error, and the claim is again quasipolynoimal time! Updated arXiv paper to follow. Update 2017-01-04: Laci has posted an update on his paper. The short version is that one small step of his analysis was not quite corr...

132. Serial Dictatorships and House Allocation (www.jeremykun.com)

I was recently an invited speaker in a series of STEM talks at Moraine Valley Community College. My talk was called “What can algorithms tell us about life, love, and happiness?” and it’s on Youtube now so you can go watch it. The central theme of the talk was the lens of computation, that algorithm...

133. One definition of algorithmic fairness: statistical parity (www.jeremykun.com)

If you haven’t read the first post on fairness, I suggest you go back and read it because it motivates why we’re talking about fairness for algorithms in the first place. In this post I’ll describe one of the existing mathematical definitions of “fairness,” its origin, and discuss its strengths and ...

134. The Boosting Margin, or Why Boosting Doesn't Overfit (www.jeremykun.com)

There’s a well-understood phenomenon in machine learning called overfitting. The idea is best shown by a graph: overfitting Let me explain. The vertical axis represents the error of a hypothesis. The horizontal axis represents the complexity of the hypothesis. The blue curve represents the error of ...

135. The Welch-Berlekamp Algorithm for Correcting Errors in Data (www.jeremykun.com)

In this post we’ll implement Reed-Solomon error-correcting codes and use them to play with codes. In our last post we defined Reed-Solomon codes rigorously, but in this post we’ll focus on intuition and code. As usual the code and data used in this post is available on this blog’s Github page. The m...

136. The Čech Complex and the Vietoris-Rips Complex (www.jeremykun.com)

It’s about time we got back to computational topology. Previously in this series we endured a lightning tour of the fundamental group and homology, then we saw how to compute the homology of a simplicial complex using linear algebra. What we really want to do is talk about the inherent shape of data...

137. What does it mean for an algorithm to be fair? (www.jeremykun.com)

In 2014 the White House commissioned a 90-day study that culminated in a report (pdf) on the state of “big data” and related technologies. The authors give many recommendations, including this central warning. Warning: algorithms can facilitate illegal discrimination! Here’s a not-so-imaginary examp...

138. Methods of Proof — Diagonalization (www.jeremykun.com)

A while back we featured a post about why learning mathematics can be hard for programmers, and I claimed a major issue was not understanding the basic methods of proof (the lingua franca between intuition and rigorous mathematics). I boiled these down to the “basic four,” direct implication, contra...

139. Weak Learning, Boosting, and the AdaBoost algorithm (www.jeremykun.com)

When addressing the question of what it means for an algorithm to learn, one can imagine many different models, and there are quite a few. This invariably raises the question of which models are “the same” and which are “different,” along with a precise description of how we’re comparing models. We’...

140. The Many Faces of Set Cover (www.jeremykun.com)

A while back Peter Norvig posted a wonderful pair of articles about regex golf. The idea behind regex golf is to come up with the shortest possible regular expression that matches one given list of strings, but not the other. “Regex Golf,” by Randall Munroe. In the first article, Norvig runs a basic...

141. Markov Chain Monte Carlo Without all the Bullshit (www.jeremykun.com)

I have a little secret: I don’t like the terminology, notation, and style of writing in statistics. I find it unnecessarily complicated. This shows up when trying to read about Markov Chain Monte Carlo methods. Take, for example, the abstract to the Markov Chain Monte Carlo article in the Encycloped...

142. The Codes of Solomon, Reed, and Muller (www.jeremykun.com)

Last time we defined the Hamming code. We also saw that it meets the Hamming bound, which is a measure of how densely a code can be packed inside an ambient space and still maintain a given distance. This time we’ll define the Reed-Solomon code which optimizes a different bound called the Singleton ...

143. Finding the majority element of a stream (www.jeremykun.com)

Problem: Given a massive data stream of $ n$ values in $ \{ 1, 2, \dots, m \}$ and the guarantee that one value occurs more than $ n/2$ times in the stream, determine exactly which value does so. Solution: (in Python) def majority(stream): held = next(stream) counter = 1 for item in stream: if item ...

144. Hamming's Code (www.jeremykun.com)

Or how to detect and correct errors Last time we made a quick tour through the main theorems of Claude Shannon, which essentially solved the following two problems about communicating over a digital channel. What is the best encoding for information when you are guaranteed that your communication ch...

145. A Proofless Introduction to Information Theory (www.jeremykun.com)

There are two basic problems in information theory that are very easy to explain. Two people, Alice and Bob, want to communicate over a digital channel over some long period of time, and they know the probability that certain messages will be sent ahead of time. For example, English language sentenc...

146. Zero-One Laws for Random Graphs (www.jeremykun.com)

Last time we saw a number of properties of graphs, such as connectivity, where the probability that an Erdős–Rényi random graph $ G(n,p)$ satisfies the property is asymptotically either zero or one. And this zero or one depends on whether the parameter $ p$ is above or below a universal threshold (t...

147. The Giant Component and Explosive Percolation (www.jeremykun.com)

Last time we left off with a tantalizing conjecture: a random graph with edge probability $ p = 5/n$ is almost surely a connected graph. We arrived at that conjecture from some ad-hoc data analysis, so let’s go back and treat it with some more rigorous mathematical techniques. As we do, we’ll discov...

148. Multiple Qubits and the Quantum Circuit (www.jeremykun.com)

Last time we left off with the tantalizing question: how do you do a quantum “AND” operation on two qubits? In this post we’ll see why the tensor product is the natural mathematical way to represent the joint state of multiple qubits. Then we’ll define some basic quantum gates, and present the defin...

149. The Quantum Bit (www.jeremykun.com)

The best place to start our journey through quantum computing is to recall how classical computing works and try to extend it. Since our final quantum computing model will be a circuit model, we should informally discuss circuits first. A circuit has three parts: the “inputs,” which are bits (either...

150. A Motivation for Quantum Computing (www.jeremykun.com)

Quantum mechanics is one of the leading scientific theories describing the rules that govern the universe. It’s discovery and formulation was one of the most important revolutions in the history of mankind, contributing in no small part to the invention of the transistor and the laser. Here at Math ...
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