Cheating in Chess and in Class

In the 24th move of the second game of the 1978 Chess Championship, a cup of blueberry yogurt was delivered to the defending champion Anatoly Karpov who offered a draw shortly thereafter. The challenger Victor Korchnoi claimed the flavor of yogurt was a coded message to Karpov and later in the tournament all food deliveries had to be decided on in advance. The good old days.

With computer chess programs now far more powerful than humans, chess cheating has become far more common and came to a head last month with the controversy between Magnus Carlsen and Hans Niemann. Did Niemann cheat to win in his win over Carlsen in St. Louis or was it just a rare upset? How can we tell?

This brings up cheating by students in class. Reports and statistics show that cheating has increased over the last few years. The pandemic played a role, but a good rule is that pandemic didn't change behaviors, rather accelerated changes already in progress. Technology has made it easier to cheat. It's difficult to near impossible to create a homework problem where someone couldn't just look up an answer. Sites like Chegg provide solutions to all sorts of problems while there are many sites where you can hire someone to write a paper or do a project for you. Advances in generative AI, like GPT-3 and GitHub co-pilot will soon make cheating as easy as clicking a button.

But it's more than technology. As students view university education less about learning and more about getting the credentials for a job, the inhibitions to cheat disappear. And while the vast majority of students don't significantly cheat, it's hard for anyone to avoid using Google when they get stuck on a problem. 

We can continue to use technology to fight the technology in a every growing arms race to catch cheaters but it can feel like a losing war. We should take solace that the students who work hard solving problems and projects will be the ones who will succeed in life. 

Alpha Tensor

In a recent post, Bill used the announcement of a new AI multiplication algorithm to discuss the applications of Strassen's famous algorithm. For this post I'd like to focus on the new algorithm itself, Alpha Tensor, the algorithm behind the algorithm, what it has actually accomplished and what it means for us theorists. 

To multiply two 2x2 matrices in the usual way you need eight multiplication steps. In 1969 Strassen surprised the world by showing how to multiply those matrices using only seven multiplications.

You can recurse on larger matrices. For 4x4 matrices you can use 7²=49 multiplications instead of the naïve 64. In general for nxn matrices you need roughly n^log₂7 ≈ n^2.81 multiplications.

No one has found an algorithm for 4x4 matrices that uses less than 49 from recursing on Strassen. Alpha Tensor does so for the special case of working over GF[2], where addition and subtraction are interchangeable. Their algorithm does not work for general fields such as the real numbers.

Here's the full table of Alpha Tensor results from the Nature paper for multiplying a nxm matrix by a mxp matrix. The Modular column is for GF[2] and the standard column is for general fields. Alpha tensor does improve on the best known for general fields for specific problems like multiplying a 3x4 matrix by a 4x5 matrix. Much of the press failed to make this distinction for 4x4 multiplication leading to some confusion.

What does this mean for theory? Recursing on 4x4 matrices now reduces the time for matrix multiplication to roughly n^2.78 nowhere close to the best theoretical upper bound of about n^2.37. The Alpha tensor result may be more practical though time will tell.

Manuel Kauers and Jakob Moosbauer shortly after Alpha Tensor announcement, reduced the 5x5 case over GF[2] to 95 multiplications. Nice to see the last word isn't by machine (yet!) but that shouldn't reduce the excitement over Alpha Tensor. Often we see a breakthrough followed by a small improvement. Note that 95 multiplications for 5x5 matrices won't give a faster asymptotic algorithm for nxn multiplication than Strassen.

What excites me the most is not the algorithm, but the algorithm to find the algorithm. Alpha Tensor uses the tools that AlphaZero used to play Chess and Go to search the large search space of potential algorithms using Monte Carlo Tree search, basically searching at random and learning and updating the probabilities of the search. Before using machine learning, we had few good approaches to searching large combinatorial spaces of this nature. 

In general, most new algorithms come from new approaches, not just from the structural decomposition we see in matrix multiplication so theorists won't be out of a job anytime soon. Nevertheless this is just another lesson that using ML has dramatically improved our ability to search through a large number of possibilities looking for a specific solution. 

The Alpha Tensor Nature paper was submitted in October of 2021. A year is eternity in the ML world. I wonder what is happening now that we don't know about.

BYTE LYNX- an awesome video game/Am I an influencer?

Tucker Bane is a friend of mine who has an AWESOME video game available

that is called BYTE LYNX.

I am curious- Can I be an INFLUENCER!

Lets find out!

At the link below there are

a) Reviews of the game.

b) Videos of the game being played.

c) The ability to purchase and download the game.

Link is here

How Not to Pass a Polygraph Test

Many years ago I was asked to serve on an advisory board for an organization that did confidential research. To be on the board I had to have US top secret clearance. The first step was filling out a lengthy form which asked deep details about every aspect of my life. Then there were the interviews with myself and many people I interacted with, especially internationally, and I have many international colleagues. Eventually I got past these steps.

The final step required taking a polygraph (lie-detector) test. So I flew to Baltimore to visit a non-descript office building near the airport. I failed the test. Twice more I went to Baltimore and failed those tests as well. 

Just to be clear, I never tried to falsify or hide information on these tests. In one case I was asked "Do you live in Atlanta?" I said no. The person administering the test stopped and said I put my address down as Atlanta. I said my mailing address was Atlanta but (at the time) I lived just north of the border in Sandy Springs. She said I should use Atlanta for the test, in other words I should lie. The test didn't go well after that.

In another case, I was asked if I was ever arrested. For the record, I have never been arrested but the answer came up as inconclusive. The administrator, different than before, trusted the machine more than me and the rest of the day didn't go well. 

Perhaps the test wasn't meant to just test whether I was telling the truth, but also my ability to keep a secret. At least that would make more sense why I failed three times. More likely I took questions too literally, a product of a mathematician's mind.

I never joined the advisory board but that wasn't the worst of it. In 2014 the Chinese hacked into the US Office of Personnel Management taking information from, among others, those who applied for security clearance. It's the main reason I keep security freezes with the credit bureaus.

Will Strassen's Matrix Mult Alg ever be practical?

All time bounds are asymptotic and really O-of.

Recall that Strassen found a clever way to multiply  two 2x2 matrices with 7 mults (and lots of adds)  leading to a matrix mult alg in n^{\log_2 7} = n^{2.87...}

Recently (see here) a deep-mind-AI found a way to multiply  two 4x4 matrices with 47 mults (and lots of adds) leading to a matrix mult alg in n^{\log_4 47} = n^{2.777...}. NOTE ADDED: The new algorithm only works over GF[2] for 4x4 matrices.

Much better is known, see our blog posts here and here.

The more advanced algorithms are complicated and have large constants so will never be practical. But Strassen's result, and now the new algorithm, SEEM to me they could be practical.

(ADDED LATER- many of the comments inform me that Strassen IS practical and IS being used. Great! Now we know!)

Thoughts about Strassen that also apply to the  new algorithm. 

1) n has to be large for Strassen to given an improvement. But as we deal with larger data sets the value of n is getting larger. 

2) People are mostly interested in sparse matrices for which there are better methods. I've heard that for a while- but is it still true? I thought ML used dense matrices. 

3) Strassen is hard to code up. Actually it doesn't look that hard to code up. However, I have never tried to code it up, so maybe there are subtle points there.

4) Strassen only works on matrices of size 2^n x 2^n. You can pad matrices out but that might kill whatever time advantage you get. (The new alg only works on  4^n x 4^n). 

5) Strassen uses recursion and there is the hidden cost of recursion. I think that is a think of the past and our younger readers do not know what I am talking about. 

6) (This is obvious) the recursion would only go down to a certain level and THEN you would use ordinary Matrix Mult. This may also add time. 

I suspect that 2 and 4 are the most important reasons Strassen (or the new algorithm) is not practical BUT I would like to hear your thoughts?

Does any package NOW use Strassen's Algorithm?

Side Note: I like to ask students if they think there is a better-than-cubic algorithm for Matrix Mult. They do not. Then I show it to them and tell them THIS is why LOWER BOUNDS are hard. You have to show that NO, nobody clever will find a trick you hadn't thought of. 

Art and Technology

Last weekend I went to one of Chicago's jewels, the Art Institute, and saw the opening of a new exhibit by Berlin-based artist Josephine Pryde entitled The Vibrating Slab referring mainly to the phone that constantly tries to gain our attention. The exhibit used photographs and sculptures to tie smart phones to prehistoric rocks. No pictures here because ironically they didn't allow us to take photos with our "slabs".

After I saw the Pryde exhibit I went to see the artist herself give a presentation. She related a story where she talked about going to the movies and seeing Top Gun: Maverick, not knowing it is a new release. Tom Cruise, who controlled a computer with hand movements in Minority Report, goes old-school in Maverick. Cruise and several young actors, through various plot contrivances, flew 20th century planes in a movie that could have taken place in the 90s. According to IMBD, at the insistence of Tom Cruise, minimal green screen and CGI aerial shots exist in the film, and even the close up cockpit shots were taken during real in-flight sequences. Old school indeed. Kind of the opposite of say the Disney series The Mandalorian filmed in a soundstage with everything generated by CGI.

Pryde's exhibit looked at the interaction with technology as art. Upstairs from Pryde's exhibit was art from technology, a series of prints that David Hockney created on another slab, the iPad, in Normandy during the early days of the Covid pandemic. 

No. 340, 21st May 2020 - David Hockney

On the way from Pryde's exhibit to the lecture I passed through the Art Institute's impressionism collection and compared real Monets with the fake one I created with Dall-E. Monet manages to capture a detailed scene with broad brush strokes--if you zoom in the detail disappears. Dall-E can't quite pull that off.

Vétheuil by Monet

Monet Dagsthul by Dall-E

Is it okay for a paper or book to say `for more on this topic see Wikipedia Entry BLAH.

 One of the proofreaders for Computational Intractability: A Guide to Algorithmic Lower Bounds

(available here)

made the following objection, which raises some questions.

I object to telling readers to see a Wikipedia Entry.  Wikipedia is marvelous, but it is unstable. I have been led astray by short-lived editorial changes made by trolls. 

The proofreader is surely correct that  `See Wikipedia entry X' should be minimized. And indeed, I have gone through all of the cases we had of such things and tried to minimize them. But there are times when there seems to be no way around it. Or maybe there is but I can't see it. 

a) I want to refer to the set of problems that are (exists R)-complete. The ONLY list I know of is on Wikipedia here.

b) I want to discuss the complexity of the video game braid. There is a nice Wikipedia page about the game braid  here. There are some sites that have videos about the game, but not reallyan  explanations of it. I DID find a site that looks pretty good, here, but is that site more stable than the Wikipedia entry? There did not seem to be an official site. (I had the same issue with the 15-puzzle and some other puzzles that do not seem to have a natural official site). 

c) I want to refer the reader to a list of algorithms for discrete log. Wikipedia has a great site on this here. Is there a good article that does the same? Is it behind paywalls?

I tend to thing that the  Wikipedia sites above are stable and accurate. It helps that they are not on controversial topics.  They should be fine. Articles that are behind paywalls are much worse. As for articles on authors websites- are they more or less stable than Wikipedia?

Machine Learning and Complexity

 

Schloss Dagstuhl by Monet by Dall-E

At Dagstuhl earlier this month, I hung out for a little bit with the participants of the other seminar, Knowledge Graphs and their Role in the Knowledge Engineering of the 21st Century. Knowledge graphs are what you would expect them to be, nodes are objects like "Berlin" and "Germany" with directed edges with labels like "capital". Think of having knowledge graphs of hundreds of millions of nodes and how that could help answer queries about the world. These secondary workshops are shorter and focus on creating a new vision, in this case how to maximize the importance of knowledge graphs in an increasing ML-focused world.

Perhaps we need such a visioning seminar for complexity. While we often get lost in the mathematical questions and techniques in our field, computational complexity is designed to understand the difficulty of solving various problems. Machine learning and advances in optimization should be changing that conversation. If you imagine a world where P = NP (and I did exactly that in chapter 2 of my 2013 book) much of what you consider is starting to happen anyway. ML does fail to break cryptography but then again, isn't this the best of all possible worlds? 

Look at what Scott Aaronson said back in 2006.

If P=NP, then the world would be a profoundly different place than we usually assume it to be. There would be no special value in “creative leaps,” no fundamental gap between solving a problem and recognizing the solution once it’s found. Everyone who could appreciate a symphony would be Mozart; everyone who could follow a step-by-step argument would be Gauss; everyone who could recognize a good investment strategy would be Warren Buffett. 

If I can be a Monet, can Mozart be far behind? ML trading by some hedge funds are beating Warren Buffett but remember if everyone trades perfectly, no one beats the average. Gauss is going to be trickier but it's coming. There's a reason Scott is spending a year at OpenAI to understand "what, if anything, can computational complexity contribute to a principled understanding of how to get an AI to do what we want and not do what we don’t want".

Is the complexity of approximating Vertex Cover of degree 3 open? (ADDED LATER-NO)

 RECALL:

A max-problem f has a A P Time App Scheme (PTAS) if there is an algorithm ALG such that

 ALG(\epsilon) \ge (1-\epsilon)f(x).

A min-problem f has a A P Time App Scheme (PTAS) if there is an algorithm ALG such that

 ALG(\epsilon) \le (1+\epsilon)f(x).

(Note that the poly can depend on epsilon so it may be something like n^{1/epsilon}.)

MAX3SAT is, given a formula with \le 3 literals per clause, find an assignment

that maximized the number of clauses satisfied.

VCB-a is Vertex cover where graphs have degree \le a

The following are known:

0) MAX3SAT is in APX.

1) The PCP paper, here, showed that if MAX3SAT has a PTAS then P=NP.

2) Papadimitriou and Yannakakis (here)  had showed much earlier that MAX3SAT \le VCB-4 with an approx preserving reduction.

3) From (1) and (2) we have that VCB-4 has a PTAS then P=NP. (VC is in APX by an easy 2-approx).

4) Clearly VCB-2 is in P.

The following seems to be open, though if you know otherwise pleae leave a comment:

Is VCB-3 a) in P? b) NPC? (ADDED LATER- NPC- See comments.) 

Is the following true: if VCB-3 has a PTAS then P=NP. (ADDED LATER- NO PTAS-See Comments)

NOTE- all of the above is true for Ind Set-4 and Dom Set-4. So that leads to more open problems.

POSTED UPDATED VERSION OF Computers and Intractability: A guide to Algorithmic Lower Bounds posted (New title)

We have posted a revised version of 

Computational Intractability: A Guide to Algorithmic Lower Bounds

by Demaine-Gasarch-Hajiaghayi

The book is here.

(For the original post about it, edited it to use the new title (see below), see HERE.) 

We  changed the title (the title above is the new one) 

since the earlier title looked too much

like the title of Garey's and Johnson's classic. While that was intentional we 

later felt that it was too close to their title and might cause confusion. 

Of course changing the title might also cause confusion; however, 

this post (and we will email various people as well) will stem that confusion. 

We welcome corrections, suggestions and comments on the book. Email us at hardness-book@mit.edu

There are two different definitions of Integer Programming. Why?

Alice and Bob have the following conversation.

===============================

ALICE: In your book you define INT PROG as, given a matrix A and vectors b,c,

find the integer vector x such that Ax\le b and c DOT x is maximized.

This is not correct! You also need x\ge 0.

BOB: Really? I always heard it without that extra constraint, though I am

sure they are equivalent and both NP-complete (Alice nods).

Where did you see it defined with that extra constraint?

ALICE:

Wikipedia entry in IP

Chapter of a book at an MIT website

Something on Science Direct

A course at Duke

An article by Papadimitriou 

An article on arxiv

The book Graphs, Networks and Algorithms by Dieter Jungnickel

Bob, do you have examples where they do not use that extra constraint. 

BOB: 

Math Works

Lecture notes from UIUC

Lecture notes from Lehigh Univ.

The book Parameterized Complexity Theory by Flum and Grohe

The book Computers and Intractability : A Guide to the Theory of NP-Completeness by Garey and Johnson

ALICE: Both of our lists are impressive. So now what? 

--------------------------------------------------------------------

(This is Bill again.)

What indeed!

1) Why are there two definitions of Int Prog?

2) When is it good to use which one? 

Monarachy: A Problem with Definitions

 As I am sure you know, Queen Elizabeth II passed away at the age of 96 recently.  I am not a royal-watcher, but I am a royal-watcher-watcher. That is, the question of why people care about the lives of these people intrigues me. A few notes

1) Was she a good Queen? People tend to think so; however, since the job is somewhat ill-defined its hard to say. 

2) The Queen is supposed to be above politics (she does not vote- I was surprised to find out that legally she can, but she really can't). We know very few of Queen Elizabeth II's opinions on political events. But the notion of political is not well defined. One would think that if she did an appeal for people to take the COVID vax that would not be political, but somehow it is (I do not know if she did such an appeal). King Charles III believes in global warming and that we need to do something about it. This again should not be political but is. 

3) She is the second longest reigning Monarch. First is King Louis XIV who first became king at the age of 4. I had a blog complaining about this here. However, there is a more interesting point I want to make. From the first to the last day of King Louis XIV reign not much had changed. Technology, politics, other things just didn't change much. By contrast the world changed A LOT between Queen Elizabeth II first and last day:

a) The British were an important power in 1952. Less so now.

b) When her father died she was in Kenya and it took 4 hours to get the news to her. Now that would be immediate. 

c) Divorce was considered bad in 1952 and is why King Edmond VIII could not be king (he wanted to marry a twice-divorced woman whose ex-husbands were still alive). And now three of the Queen's children have been divorced.

d) Gay people.. enough said. There has even been a royal gay wedding, see here

Black people (can't call them African-Americans), Women,... you fill it in. 

e) When Charles wanted to get married it seemed to be important that he marry a virgin. We cannot imagine this mentality anymore. When Prince William and Kate got married they were already living together and this was NOT an issue for ANYONE. I looked up what the Church of England thought of it and all I got was some very bland comments like That's what young people do nowadays. 

3) Is the monarchy a good thing? As an American I feel I do not have a right to an opinion. If the citizens of the United Kingdom approve of the monarch (polls show they do) then who am I do tell them they are wrong? Even so, lets look at reasons for it

a) Tourism. It has been said that the Monarchy leads to MONEY from tourism. So it is worth the price? Nobody seems to know and it would be hard to tell. However, I don't think the citizens of the United Kingdom view  money as the reason for Monarchy. The American analog is giving Disneyland tax breaks to be in Florida which generates jobs. I doubt they think of the Monarchy in those mundane transactional terms. 

b) CS Lewis said 

Where men are forbidden to honour a king they honour millionaires, athletes, or film stars instead: even famous prostitutes and gangsters. For spiritual nature, like bodily nature, will be served; deny it food and it will gobble poison.

This is  bit odd- they must all pretend to like the monarchy to make it work. A long time ago when Charles and Dianna were both having affairs, 80% of the citizens the United Kingdom thought that was okay so long as they are discreet so the people don't find out. But- those ARE the people.

Also odd- CS Lewis was a theologian and a  believing Christian; however, his comment above can apply to God as well as to Kings. 

Thirty Years of Dagstuhl

 

Dagstuhl old-timers at the original castle

I'm back at Dagstuhl for the seminar on Algebraic and Analytic Methods in Computational Complexity. My first seminar at Dagstuhl was back in 1992. I've been coming for thirty years and have been here roughly thirty times. My last trip was pre-covid (virtual Dagstuhls don't count) and I really needed this chance to hang out and talk complexity with colleagues old and new.

Some changes since my last trip. The room doors have locks (there are rumors of an incident). You have to create your own keycard on a new machine logging into your Dagstuhl account. I had a long random password through a password manager and it was not so easy as process.

The main conference room has been updated with tech for hybrid meetings, and new led lights. Books were removed from the library to create a coffee breakout space.

No Bill this time so no typecasts. Still the best part of the week is talking and hearing about complexity. Today I learned about the orientations of Sperner's lemma, that there is one more triangle oriented according to the direction of the corner vertices than those oriented the other way. Christian Ikenmeyer used this fact to motivate a study of closure properties of #P-functions.

The NIST Process for Post-Quantum Cryptography

Guest post by Jonathan Katz

Over the past few months there have been several interesting developments in the NIST post-quantum standardization process.

By way of background, since the advent of Shor's algorithm in 1994 we have known that a large-scale, general-purpose quantum computer would be able to break all currently deployed public-key cryptography in (quantum) polynomial time. While estimates vary as to when (or even whether!) quantum computers will become a realistic threat to existing public-key cryptosystems, it seems prudent to already begin developing/deploying newer "post-quantum" schemes that are plausibly secure against quantum computers.

With the above in mind, NIST initiated an open process in 2017 for designing post-quantum cryptographic standards. Researchers from around the world submitted candidate algorithms for public-key encryption/key exchange and digital signatures. These were winnowed down over a series of rounds as cryptographers publicly debated the relative merits of different proposals, or showed security weaknesses in some candidates.

On July 5 of this year, NIST announced that it had selected four of the submissions as finalists for standardization. Only one candidate for public-key encryption was chosen, along with three digital signature schemes. Three of the four selected algorithms rely on the hardness of lattice problems; the only non-lattice scheme is a hash-based signature scheme. (It is possible to build digital signatures using "symmetric-key" assumptions alone.) In addition, four other public-key encryption schemes not based on lattices were designated for further study and possible standardization at a later point in time.

Less than one month later, Castryck and Decru announced a classical attack on SIKE, one of the public-key encryption schemes chosen for further study. SIKE is based on a conjectured hard problem related to isogenies on supersingular elliptic curves. The attack was not just theoretical; the researchers were able to implement the attack and run it in less than a day or less, depending on the security level being considered. Details of the attack are quite complex, but Galbraith gives a high-level overview. Subsequent improvements to the attack followed.

It is worth adding that the above follows an entirely classical attack shown roughly six months earlier on Rainbow, another submission to the NIST standardization process that made it to the 3rd round. (Rainbow is a signature scheme that relies on an entirely different mathematical problem than SIKE.) For completeness, note that none of the four finalists are impacted by any of these attacks.

A few reflections on the above:

• It is amazing that the factoring and RSA problems are still hard (for classical computers), more than 40 used after they were proposed for cryptography. The same goes for the discrete-logarithm problem (in certain groups).
• It is not easy to find other hard mathematical problems! Code-based cryptography has been around about as long as factoring, but has been somewhat unpopular for reasons of efficiency. Lattice-based cryptosystems still seem to give the leading candidates.
• We need more (non-cryptographers) studying cryptographic assumptions. The attacks on SIKE involved deep mathematics; attacks on lattice problems may involve algorithmic ideas that cryptographers haven't thought of.

Computational Intractability: A Guide to Algorithmic Lower Bounds. First draft available! Comments Welcome!

(This post is written by Erik Demaine, William Gasarch, and Mohammad Hajiaghayi)

In 1979 Garey and Johnson published the classic

        Computers and  Intractability: A Guide to NP-Completeness

There has been A LOT of work on lower bounds since then.

Topics that were unknown in 1979 include

Parameterized Complexity,

 Lower bounds on approximation,

Other hardness assumptions (ETH, 3SUM-conjecture, APSP-conjecture, UGC, Others), 

Online Algorithms,

Streaming Algorithms, 

Polynomial Parity Arguments, 

Parallelism, and 

Many new problems have been shown complete or hard in NP, PSPACE, and other classes.

Hence a sequel is needed. While it is impossible for one book to encompass all, or even a large fraction, of the work since then, we are proud to announce a book that covers some of that material:

Computational Intractability: A Guide to Algorithmic Lower Bounds

by Erik Demaine, William Gasarch, and Mohammad Hajiaghayi. MIT Press. 2024

See HERE for a link to a first draft.

We welcome corrections, suggestions and comments on the book. Either leave a comment on this blog post or emailing us at hardness-book@mit.edu

20 Years of the Computational Complexity Weblog

I first posted on this blog twenty years ago today, still the oldest and longest running weblog in theoretical computer science, possibly in all of computer science. In those twenty years we've had nearly 3000 posts and over 23,000 comments and 10,000,000 page views. Bill Gasarch joined me officially as a co-blogger over 15 years ago on March 30, 2007. 

We've seen major results in computational complexity but the biggest challenges remain, in particular major separations of complexity classes. We've also had a front row seat to a computing revolution with  the growth of cloud and mobile computing, social networks connecting us for better or for worse, and incredible successes of machine learning. It's almost as though we've been handed an oracle that gives us much of the goodness of P = NP while leaving cryptography intact. 

What will the next twenty years bring? We'll be watching, posting and tweeting. Hope you keep reading and commenting. 

Conference Modality

We have had an almost normal summer conference season, for some sense of normal. At one of those conferences I participated in an hybrid conversation about whether the conference should be in-person, virtual or hybrid the following year. Here are some general thoughts.

In-Person

The traditional conference format. People travel from near and far to a hotel, conference center or campus location. Talks given in large rooms, often in parallel. A reception, some social activities, participants gathering in small groups to go out for meals. 

Positives: In-person maximizes interaction between participants. Being physically away from your home means you can focus your time on the conference and your fellow participants. This was more true before the mobile devices/laptops/internet days, but still most participants will spend more time on-site than on-line.

Negatives: Expensive--with registration, hotel and air fare, even a domestic participant could have to pay $2000 or up, higher for those traveling internationally. Visas can be hard to get. Some still feel unsafe in large groups. People often leave early, pity the last speakers. And don't forget the carbon footprint. 

As countries declare war on other countries or states deny certain rights, there is a push against meetings in certain places. Note the disclaimer for next year's FCRC. You might upset some people if you have conferences at these locations (and others if you don't).

Virtual

Virtual conferences would never in the past have been taken seriously but Covid forced our hands. 

Talks are streamed or pre-recorded. Some interaction with chats in talks, zoom get togethers or though a systems like virtual chair. Even if we had a perfect "metaverse" experience where we could get together as though we were in person, not being there in person means we wouldn't make it a priority.

The big advantages are costs are low, it's easy to attend talks, and no danger of spreading disease. Still a virtual conference can feel too much like just a collection of streamed and recorded talks.

Hybrid

So let's make the conference hybrid and have the best of both worlds. Alas, it doesn't work out that way. It's nearly impossible to have good interaction between in-person and virtual participants--basically you have to run two separate meetings. Do you allow virtual talks or require an author to show up in person. 

How do you set prices? Lower prices for virtual increases assess but decreases in-person attendance. Participants (or their advisors) might opt to save expenses and attend virtually instead of in-person, reducing the networking opportunities for everyone. 

Most conferences tend to take the hybrid route to avoid the complaints if one went fully in-person or virtual, but hybrid just pretty much guarantees a mediocre experience for all.

Opinion

My suggestion is some years run the conference virtually and others in hybrid. We already have too many conferences, a byproduct of our field using conferences as the primary publication venue. I suggest following conferences like the International Congress of Mathematicians or the Game Theory World Congress, held every four years. If the main conference of a field is held every four years, researchers, particularly senior researchers, make a bigger effort to be there. You can have the virtual meetings the other years so researchers, particularly students, can continue to present their work.

No easy solutions and CS conferences have not worked well for years. Maybe the struggle to define future conferences will allow us to focus more on the connecting researchers than just "journals that meet in a hotel".

A non-controversial question about the Documents Donald Trump had in his house

This is a non-partisan post. In the interest of disclosure I will divulge that I do not think private citizens should have top secret government documents in their house.

One phrase I kept hearing in the reporting was (I paraphrase and may have the number wrong)

                                  The FBI removed 15 boxes of documents

Documents? Like--- on paper? 

a) Are the documents also online someplace? Perhaps they intentionally are not so that they can't be hacked. 

b) Is having top secret documents only on paper safer than having them in electronic form? Normally I would think so. Donald Trump  having them is a very unusual case. 

c) Having to store all of those documents on paper would seem to have storage problems. I can imagine someone with NO bad purposes making copies and taking them home since they are tired of only being about to read them in a special room. 

d) A problem with having them ONLY on paper is that if an accident happens and they get destroyed there is no backup. Or is there? Are there copies somewhere? That leads to twice the storage problems. 

e) There is a tradeoff of security and convenience. Is having the documents only on paper is an extreme point on the tradeoff, but it may be the right one. It may depend on how important it is to keep the documents secret. 

f) I've heard (apocryphal?) stories about some top secret document also available in public though quite legal sources (e.g., a physics journal that discusses nuclear stuff).  Does the government make to much classified? If so then the problem arises of people not taking the classification seriously and getting careless. I doubt that is what happened here. 

g) The question I am most curious about is why did he take them? For most of his other actions his motivations are clear (e.g., he is pushing STOP THE STEAL since he wants to be president). But for this one its not clear. Unfortunately,  I doubt we'll ever find out. Maybe the answer is in some documents either on paper or electronic. 

The Godfather of Complexity

Juris Hartmanis 1928-2022

On Friday, July 29th, I was in the immigration line at an airport in Mexico. My phone rang with Bill Gasarch on the Caller ID but starting vacation I declined the call. The voicemail gave me the sad news that Juris Hartmanis, the person who founded computational complexity and brought me into it passed away earlier that day. I messaged Bill and told him to write an obit and I'd follow with something personal when I returned.

Hartmanis and Stearns in 1963

In November 1962 Hartmanis, working with Richard Stearns at the GE Labs in Schenectady, determined how to use Turing machines to formalize the basic idea to measure resources, like time and memory, as a function of the problem being solved. Their classic Turing-award winning paper On the Computational Complexity of Algorithms, not only gave this formulation but showed that increasing resources increased the problems one can solve. The photo above, from a post celebrating the 50th anniversary of the paper, shows Hartmanis and Stearns with the main theorem of their paper on the board.

Twenty-one years later, a junior at Cornell University still trying to find his way took undergraduate theory from the man himself. Juris brought the topics to life and I found my passion. At the beginning of the class, he said the highest grade usually went to an undergrad followed by the grad students in the class. I was a counterexample, as I had the second highest grade. Never did find out who beat me out.

In spring of my senior year, 1985, I forgave the traditional senior-slump Wines for graduate complexity with Juris. He focused the course around the isomorphism conjecture he developed with his student Len Berman, which implied P≠NP, and Hartmanis believed using the conjecture might lead to settling P v NP. He offered an automatic A to anyone who could prove the isomorphism conjecture. I guess any other proof of P≠NP only warranted a B?

I would later be obsessed by the isomorphism conjecture as an assistant professor, coming up with not one but two oracles making it true. The isomorphism conjecture didn't end up settling P vs NP, but then again neither did any other approach.

It wasn't just me, there was a reason that many of the great American complexity theorists, including Ryan Williams, Scott Aaronson and my own PhD advisor Michael Sipser, were undergrads at Cornell. Many more were PhD students of Hartmanis.

Juris Hartmanis had a certain gravitas in the community. Maybe it was his age, the way he dressed up, his seminal research in the field, or just that Latvian accent. He founded the CS department at Cornell in the 60s and served as head of the CISE directorate at the National Science Foundation in the 90s. His 60th birthday party at the 3rd Structures in Complexity conference (now the Computational Complexity Conference) was the only time I've seen complexity theorists in ties.

Juris Hartmanis (center) being toasted by Janos Simon

A few of my favorite Hartmanis quotes.

• "We all know P is different than NP. We just don't know how to prove it." - Still true.
• "I only make mistakes in the last five minutes of the class." - Sometimes he made a mistake with ten minutes left but only admit it in the last five minutes.
• "Primality is a problem not yet know to be in P but is hanging on by its fingernails with its grip continuing to loosen each day." - Juris Hartmanis said this in 1986, with primality hanging on for another 16 years.

Thanks Juris for creating the foundations of our field and inspiring so many people, yours truly included, to dedicate ourselves to it.

Much more to read:

• In 1981 Hartmanis wrote a personal article for the Annals of History of Computing describing the birth of computational complexity and his role in it.
• A CACM interview with Hartmanis gives a personal story of his path from Latvia to Kansas City to Pasadena to Schenectady to Ithaca and DC.
• Obits by Cornell, Ryan Williams and Dick Lipton and Ken Regan.
• My old blog posts celebrating Hartmanis' 90th birthday and the 50th anniversary of Hartmanis-Stearns,
• And the many retrospectives, remembrances, special issues and conferences to come.

The Held Prize for comb. opt. AND Disc Opt AND Alg AND Complexity theory AND related parts of CS.

 Dan Spielman asked me to blog about the Held Prize. I first present what he send me, and then have some thoughts.

FROM DAN: 

--------------------------------------------------------------------------------------------------

Nominations are now being accepted for the National Academy of Sciences’ 2023 Michael and Sheila Held Prize. The Held Prize honors outstanding, innovative, creative, and influential research in the areas of combinatorial and discrete optimization, or related parts of computer science, such as the design and analysis of algorithms and complexity theory. This $100,000 prize is intended to recognize recent work (defined as published within the last eight years). Additional information, including past recipients, eligibility requirements, and more, can be found at here.

All nominations must be submitted online. Unless otherwise stated, the following materials must be submitted: 

A letter from the nominator describing the candidate's work and why he or she should be selected for the award. No more than three (3) pages.

Curriculum vitae. No more than two (2) pages (similar to CVs included with NSF proposals).

Bibliography listing no more than twelve (12) of the nominee's most significant publications.

Suggested citation. A 50-word summary stating why the nominee should be considered for this award. (Citation

examples)

Two letters of support. Support letters must be written by individuals from institutions outside both the

nominator's and the nominee’s institution. Up to three letters of support are accepted.

Nominations will be accepted through Monday, October 3, 2022. Please help spread the word that the nomination process is underway. 

----------------------------------------------------------------------------------------------------

BILL COMMENTS

1) The scope seems rather broad (Dan confirmed this in private email) in that its Comb Opt AND Discrete Opt OR related fields like algorithms and complexity theory. 

2) The research has to be Outstanding AND Innovative AND creative AND influential. That seems hard to do :-(  If they made it an OR instead of an AND I may ask someone to nominate me for my Muffin Work. It does use 0-1 programming!

3) The past winners are, of course, very impressive. But there is one I want to point out to emphasize that the scope is broad: Amit Sahai won in 2022, and here is what the webpage says about it:

For a leading role in development of cryptographic Software Obfuscation and its applications, starting from initial conception of "Indistinguishability Obfuscation" and culminating in new constructions based upon well-founded cryptographic assumptions. These breakthroughs highlight how computational complexity can enable secrecy while computing in insecure environments.

4) Comb Opt and Discrete Opt seem to be Operations Research. So this inspires the following question:

What are the similarities and differences between Operations Research and Research on Algorithms? 

I tend to think of Operations Research  as being more tied to the real world- but is that true?

5) Not enough 2-letter combinations  for what you want to say: I had to use the term Operations Research instead of the abbreviation OR since I was using OR for or. Oh well.