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The World Turned Upside Down

July 10, 2016


Some CS reflections for our 700th post

Lin-Manuel Miranda is seen in New York, New York on Tuesday September 2, 2015.
MacArthur Fellowship source

Lin-Manuel Miranda is both the composer and lyricist of the phenomenal Broadway musical Hamilton. A segment of Act I covers the friendship between Alexander Hamilton and Gilbert du Motier, the Marquis de Lafayette. This presages the French co-operation in the 1781 Battle of Yorktown, after which the British forces played the ballad “The World Turned Upside Down” as they surrendered. The musical’s track by the same name has different words and melodies.

Today we discuss some aspects of computing that seem turned upside down from when we first learned and taught them.

Yesterday was halfway between our Fourth of July and France’s Bastille Day, and was also the last day of Miranda performing the lead on-stage with the original Hamilton company. They are making recordings of yesterday’s two performances, to be aired at least in part later this year. A month ago, Miranda wrote an op-ed in the New York Times against the illegal (in New York) but prevalent use of “bots” to snap up tickets the moment they become available for later marked-up resale.

This is also the 700th post on this blog. It took until 1920 for a Broadway show of any kind to reach 700 performances. The Playbill list of “Long Runs on Broadway” includes any show with 800 or more performances. That mark is within our reach, and our ticket prices will remain eminently reasonable.

What Has Changed?

This list is just what strikes us now—far from exhaustive—and we invite our readers to add opinions about examples in comments.


NP-Complete Means Easy

Forty-five years ago, Dick Karp showed how the difficulty of SAT represented by NP-completeness spreads to other natural problems. As the number of complete problems from many areas of science and operations research soared into the thousands by the 1979 publication of the book Computers and Intractability, people regarded NP-completeness as tantamount to intractability.

Today the flow is in the other direction. Dick Karp himself has been among many in the vanguard—I remember his talk on practical solvability of Hitting-Set problems at the 2008 LiptonFest and here is a relevant paper. We now reduce problems to SAT in order to solve them. SAT-solvers that work in many cases are big business. In some whole areas the SAT-encodings of major problems are well-behaved, as we remarked about rank-aggregation and voting theory in the third section of this post from last October. The solvers can even tackle huge problems. Marijn Heule, Oliver Kullback, and Victor Marek proved that every 2-coloring of the interval {[1,2,\dots,7285]} has a monochromatic Pythagorean triple in a proof of over 200 terabytes in uncompressed length.


Quadratic Time Means Hard

Quadratic time is notionally on the low end of polynomial time, and “polynomial time” has long been used as a synonym for “easy.” But as the amount of data we can and need to handle has mushroomed, the difference in scaling between quasi-linear and quadratic is more and more felt. This difference has even been argued for cryptographic security. A particular definition of quasi-linear time is {n(\log n)^{O(1)}} time as named by Claus Schnorr for his theorem on quasi-linear completeness of SAT; see also this.

In genomics the quadratic time of algorithms for full edit-distance measures is felt enough to warrant approximative methods, as we covered in our memorial a year ago for Alberto Apostolico. This also puts meaning behind theoretical evidence that {O(n^2)} time for edit distance cannot be improved.


These two items seem to contradict each other, but point up a difference in scale between data and logical control. Often a thousand data points are nothing. A formula with a thousand clauses can say a lot.


AI Winter Into Spring

My first doctoral student had been working on neural networks before I became his advisor in 1991, and I remember the feeling of their being under a cloud. The so-called AI Winter traced in part to lower bounds shown against certain shallow neural nets in the 1969 book Perceptrons by Marvin Minsky and Seymour Papert. We discussed complexity aspects of this in our memorial of Minsky last January.

Since then what has emerged is that composing a bunch of these nets, as in a convolutional neural network (CNN), is both feasible and algorithmically effective. The recent breakthrough on playing Go is just a headline among many emerging applications of CNNs and larger systems. We are not saying neural nets and deep learning are the be-all or anything more than a “cartoon” of the brain, but rather noting them among many reasons that AI and machine learning are resurgent.


Data-Parallelism and Functional Languages Are Revenant

The same AI-winter article on Wikipedia mentions the collapse of Lisp-dedicated systems in 1987, and more widely, many companies devoted to data-parallel architectures “left nothing but their logos on coffee mugs” as a colleague once put it. Subsequently I perceived signs of stagnation in functional languages in the late 1990s and early 00s. This lent a ghostly air to John Backus’s famous 1978 Turing Award lecture, “Can Programming Be Liberated From the von Neumann Style?”

Unlike the revenant in last year’s award-winning movie of that name, this one has come back with a different body. Not a large-scale dedicated machine system, but rather the pan-spectral pervasion we call the Cloud. A great lecture we heard by Mike Franklin on Amplab activities highlighted the role of programs written in the functional language Scala running on the Apache Spark framework.


A common thread in all these items is the combined efficacy and scalability of algorithmic primitives whose abstract forms characterize quasi-linear time: sorting, parallel prefix sum (as one of several forms of map-reduce), convolution, streaming count-sketching, and the like.

Open Problems

We considered mentioning some subjects that have seen changes such as digital privacy and block ciphers, but maybe these are not so “upside-down.” Doubtless we are missing many more. What developments in computing have carried shock on the order of the discovery that neutrinos have mass in particle physics? We invite your suggestions and opinions.

Here also is a web folder of photos from Dick’s wedding and honeymoon.

Getting to the Roots of Factoring

June 29, 2016


We revisit a paper from 1994

RichardRelaxed2

Richard Lipton is, among so many other things, a newlywed. He and Kathryn Farley were married on June 4th in Atlanta. The wedding was attended by family and friends including many faculty from Georgia Tech, some from around the country, and even one of Dick’s former students coming from Greece. Their engagement was noted here last St. Patrick’s Day, and Kathryn was previously mentioned in a relevantly-titled post on cryptography.

Today we congratulate him and Kathryn, and as part of our tribute, revisit a paper of his on factoring from 1994.
Read more…

Theory In The Time of Big Data

June 18, 2016


What is the role of theory today?

AnnaAtri

Anna Gilbert and Atri Rudra are top theorists who are well known for their work in unraveling secrets of computation. They are experts on anything to do with coding theory—see this for a book draft by Atri with Venkatesan Guruswami and Madhu Sudan called Essential Coding Theory. They also do great theory research involving not only linear algebra but also much non-linear algebra of continuous functions and approximative numerical methods.

Today we want to focus on a recent piece of research they have done that is different from their usual work: It contains no proofs, no conjectures, nor even any mathematical symbols.
Read more…

Polynomial Prestidigitation

June 15, 2016


How some longstanding open problems were made to disappear

CrootLevPach

Ernie Croot, Vsevolod Lev, and Péter Pach (CLP) found a new application of polynomials last month. They proved that every set {A \subseteq U = \mathbb{Z}_4^n} of size at least {|U|^{1-\epsilon}} has three distinct elements {a,b,c} such that {2b = a + c}. Jordan Ellenberg and Dion Gijswijt extended this to {\mathbb{F}_q^n} for prime powers {q}. Previous bounds had the form {|U| n^{-c}} at best. Our friend Gil Kalai and others observed impacts on other mathematical problems including conjectures about sizes of sunflowers.

Today we congratulate them—Croot is a colleague of Dick’s in Mathematics at Georgia Tech—and wonder what the breakthroughs involving polynomials might mean for complexity theory.
Read more…

Progress On Modular Computation

May 28, 2016


New results on computing with modular gates

ChenPapakonstantinou

Shiteng Chen and Periklis Papakonstaninou have just written an interesting paper on modular computation. Its title, “Depth Reduction for Composites,” means converting a depth-{d}, size-{s} {\mathsf{ACC^0}} circuit into a depth-2 circuit that is not too much larger in terms of {d} as well as {s}.

Today Ken and I wish to talk about their paper on the power of modular computation.
Read more…

Making Public Information Secret

May 20, 2016


A way to recover and enforce privacy

ScottMcNealyHighNoon
McNealy bio source

Scott McNealy, when he was the CEO of Sun Microsystems, famously said nearly 15 years ago, “You have zero privacy anyway. Get over it.”

Today I want to talk about how to enforce privacy by changing what we mean by “privacy.”
Read more…

Coins on a Chessboard III

May 14, 2016


From knight’s tours to complexity

Warnsdorff3
Von Warnsdorf’s Rule source

Christian von Warnsdorf did more and less than solve the Knight’s Tour puzzle. In 1823 he published a short book whose title translates to, The Leaping Knight’s Simplest and Most General Solution. The ‘more’ is that his simple algorithm works for boards of any size. The ‘less’ is that its correctness remains yet unproven even for square {n \times n} boards.

Today we consider ways for chess pieces to tour not 64 but up to {2^{64}} configurations on a chessboard.
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