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#ComputationalComplexity

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Taught a bonus lecture for a course on Algorithms for NP-hard Problems today. Material won't be on the exam, so technically, this lecture was "just for fun".

Quite some pressure to make coming to class on Friday morning 8:45am worth it for a gang of 20-year-olds.

Students were a hoot. They were listening actively and participating. Great to meet them. Had a blast 🙂

Afterwards, some of them thanked me for the "really great lecture" 🥺

Ab-so-lute-ly exhausted now.

Given a decision problem Π, I need a name for the polytope constructed by taking the convex hull of the characteristic vectors (or binary inputs of fixed length) corresponding to "yes" instances. If nobody has a better suggestion I might use "the characteristic polytope of Π". OTOH, for $reasons I'd much rather use something more standard / widespread.

Continued thread

Turns out rational approximation of irrationals is related to the Hartmanis-Stearns '65 Conjecture on real-time computability, from the same paper that introduced basic complexity classes like DTIME(f(n)). And sqrt(2) makes an appearance!

The Hartmanis-Stearns real-time computability conjecture is (equivalent to):

For a real number r, if there is an algorithm that computes the first n digits in O(n) time (for all n), then r is either rational or transcendental.

And, coming from this, sqrt(2) has an even cooler connection:

If the 1st n digits of sqrt(2) can't be computed in O(n) time, then n-bit integers can't be multiplied in O(n) time. -Lipton & Regan rjlipton.wpcomstaging.com/2012

(But...sqrt(2) probably isn't the only number for which the above is true. Still a cool connection, I think.)

Gödel's Lost Letter and P=NP · Why The Hartmanis-Stearns Conjecture Is Still OpenA missed? or forgotten? connection from 1976 Richard Brent is an illustrious Australian mathematician and computer scientist. He is known for Brent’s Theorem, which shows that a parallel algo…

I just finished listening to the latest If Books Could Kill episode, this one about Michael Lewis' book on Sam Bankman-Fried: twitter.com/IfBooksPod/status/

Saying that chess is too simple is a huge red flag for those aware that it's EXPTIME-hard. (sciencedirect.com/science/arti) If you can solve Chess scenarios effortlessly, then you can solve all sorts of extremely heavy computational problems that elude us.

@rottenindenmark

X (formerly Twitter)If Books Could Kill (@IfBooksPod) on XEpisode 29: Going Infinite When Michael Lewis looked at Sam Bankman Fried, he saw a young, enigmatic genius who was remaking the financial world. A federal jury strongly disagreed. https://t.co/fJVaIou8Ui

It's well known you can find a solution to the linear Diophantine equation

a1x1+a2x2++anxn=c

in polynomial time using the Extended Euclidean Algorithm. But I haven't been able to find a clear answer for the complexity of finding a non-negative integer solution, that is, xi0.

The unbounded knapsack problem is NP-complete, and this is the case where the weights and costs are equal, but I'm not sure that the NP-hardness reduction applies given that additional constraint.

I have also found a statement that the "multidimensional knapsack problem" is NP-hard even with a single row, which seems to match this, but I lack a copy of Papadimitriou and Steiglitz to verify that statement, and can't figure out the reduction.

If this problem is NP-hard, then I'm also interested in showing that a related problem is also NP-hard: if we have one non-negative solution, can we find a second one? I'm trying to answer a question about the special case c=i=1nai.

Two of my papers recently got submitted (kyleburke.info/?main=research) so I now have some I implemented that I can share.

The first of these is Paint Can: kyleburke.info/DB/combGames/pa. This game has stacks of bricks: blue, red, green, and gray. A player can either take a green brick or a brick of their color (Red or Blue). (Neither player can choose gray bricks.) When they remove a brick, that also removes all bricks above that one. Any pile that has non-green bricks also has a paint can on top of it. When any brick is removed, the can spills and all the remaining bricks in that stack become green.

is novel because each component has very low stakes (all options are to nimbers or zero) but the game is still complicated (NP-hard) to play optimally.

kyleburke.infoPaint Can

P vs. NP: The Biggest #Puzzle in #ComputerScience

"Are there limits to what #Computers can do? How complex is too complex for #Computation? The question of how hard a problem is to solve lies at the heart of an important field of computer science called #ComputationalComplexity."

youtube.com/watch?v=pQsdygaYcE

I'm going to be talking about Algorithmic CGT in India in a few weeks. I really want to start with the fundamentals of , so I want to present the Boolean Formula Game (en.wikipedia.org/wiki/Formula_). Whenever I talk about a game, I like to play it with the audience.

So... I coded a working version: kyleburke.info/DB/combGames/bo

It's not particularly fun, mostly because it's often very easy to win as the True player. (I didn't implement any deep strategies to generate more interesting formulas. Advice is welcome!)

Nevertheless, I'm very excited to use this next week and in future talks.

en.wikipedia.orgFormula game - Wikipedia

In our effort to put courses online, we continue lectures on Algorithmic Lower Bound Course. Now you can watch

Lesson 4-11: Algorithmic Lower Bounds by Mohammad Hajiaghayi - NP-Completeness and Beyond

(FEEL FREE TO SUBSCRIBE TO YOUTUBE @hajiaghayi FOR FUTURE LESSONS Premiering on WEDNESDAYS)

youtu.be/VZyffnAb1r0 (Lesson 4: 3-Partition Problem & Proving NP-Hardness)

youtu.be/4fCD9_1eQw0 (Lesson 5: Puzzle Problem NP-Hardness & 3-Partition)

youtu.be/FIyEj72-UJQ (Lesson 6: 3-SAT Problem & Proving NP-Hardness)

youtu.be/tbSJzaKx2pA (Lesson 7: Puzzle Problem NP-Hardness via 3-SAT)

youtu.be/voRVebBsh94 (Lesson 8: Fine-grained Subcubic Complexity: Part 1)

youtu.be/gRURSM6QARo (Lesson 9: Fine-grained Subcubic Complexity: Part 2)

youtu.be/qPw82bTAXkc (Lesson 10: Fine-grained Subquadratic Complexity 1)

youtu.be/C6j4avVkI7U (Lesson 11: Fine-grained Subquadratic Complexity 2)

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For comprehensive handwritten lecture notes on this course, visit the instructor's website:

cs.umd.edu/~hajiagha/
The course textbook "Computational Intractability: A Guide to Algorithmic Lower Bounds" by Demaine, Gasarch, and Hajiaghayi is available for free at:

hardness.mit.edu/