Podcasts about np complete

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In the last episode, we gave you the basic theory. Now we're going to show you how Computational Theory is actually used in real life. We'll discuss the various computational classes that exist and one special class in particular: NP-Complete. Using reducibility (as discussed in the previous episode) we can prove that this is a universal class of problems. This provides us evidence (but not a proof!) that many algorithms are too slow to be tractable (i.e. return a result in a useful amount of time.) Finally, we'll discuss the startling fact that some problems can't be computed at all because the laws of physics don't allow it. Youtube version with optional visuals: https://www.youtube.com/watch?v=rVpM8XOwmz4 Note: Due to the nature of these Computational theory episodes, it might be helpful to see the Youtube visuals. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/four-strands/support

At least to me, Jersey has always been the shred capitol of the Northeast with Rochester, NY being a close rival for technical superior, “extreme” metal players. This is the state that gave us Human Remains, Ripping Corpse and Dim Mak to name a few. Despite what Chris Alfano and Matt Lupo may believe, I claim that East of the Wall are part of that Jersey lineage of technical driven, progressive metal bands, with the emphasis more on the progressive elements.Despite having spent an hour with these guys, I’m still unclear as to when the band actually started and who was in the original lineup; the band seemed to spring, full-formed from two other bands in the area called The Postman Syndrome and Day Without Dawn.Poised to release their brand-new full-length, NP-Complete on Translation Los, Chris and Matt met up with me somewhere in the wilds of New Jersey to hand out and talk about the record and a variety of other topics.Intro: “Tell Them I’m Sorry” – East of the Wall, NP-CompleteOutro: “ N of 1” – East of the Wall, NP-Complete See acast.com/privacy for privacy and opt-out information.

Algorithms with similar runtimes are said to be in the same complexity class. That runtime is measured in the how many steps an algorithm takes relative to the input size. The class P contains all algorithms which run in polynomial time (basically, a nested for loop iterating over the input).  NP are algorithms which seem to require brute force.  Brute force search cannot be done in polynomial time, so it seems that problems in NP are more difficult than problems in P.  I say it "seems" this way because, while most people believe it to be true, it has not been proven.  This is the famous P vs. NP conjecture.  It will be discussed in more detail in a future episode. Given a solution to a particular problem, if it can be verified/checked in polynomial time, that problem might be in NP.  If someone hands you a completed Sudoku puzzle, it's not difficult to see if they made any mistakes.  The effort of developing the solution to the Sudoku game seems to be intrinsically more difficult.  In fact, as far as anyone knows, in the general case of all possible examples of the game, it seems no strategy can do better on average than just random guessing. This notion of random guessing the solution is where the N in NP comes from: Non-deterministic.  Imagine a machine with a random input already written in its memory.  Given enough such machines, one of them will have the right answer.  If they all ran in parallel, one of them could verify it's input in polynomial time.  This guess / provided input is often called a witness string. NP is an important concept for many reasons.  To me, the most reason to know about NP is a practical one.  Depending on your goals or the goals of your employer, there are many challenging problems you may attempt to solve.  If a problem you are trying to solve happens to be in NP, then you should consider the implications very carefully.  Perhaps you'll be lucky and discover that your particular instance of the problem is easy.  Sudoku is pretty easy if only 2 remaining squares need to be filled in.  The traveling salesman problem is easy to solve if you live in a country where all roads for a ring with exactly one road in and out. If the problem you wish to solve is not trivial, or if you will face many instances of the problem and expect some will not be trivial, then it's unlikely you'll be able to find the exact solution.  Sure, maybe you can grab a bunch of commodity servers and try to scale the heck out of your attempt.  Depending on the problem you're solving, that might just work.  If you can out-purchase your problem in computing power, then problems in NP will surrender to you.  But if your input size ever grows, it's unlikely you'll be able to keep up. If your problem is intractable in this way, all is not lost.  You might be able to find an approximate solution to your problem.  Good enough is better than no solution at all, right?  Most of the time, probably.  However, some tremendous work has also been done studying topics like this.  Are there problems which are not even approximable in polynomial time?  What approximation techniques work best?  Alas, those answers lie elsewhere. This episode avoids a discussion of a few key points in order to keep the material accessible.  If you find this interesting, you should next familiarize yourself with the notions of NP-Complete, NP-Hard, and co-NP.  These are topics we won't necessarily get to in future episodes.  Michael Sipser's Introduction to the Theory of Computation is a good resource.  

In this recitation, problems related to NP-Completeness are discussed.

Materials Available here: https://media.defcon.org/DEF%20CON%2023/DEF%20CON%2023%20presentations/DEFCON-23-John-Seymour-Quantum-Classification-of-Malware-UPDATED.pdf Whitepaper here: https://media.defcon.org/DEF%20CON%2023/DEF%20CON%2023%20presentations/DEFCON-23-John-Seymour-Quantum-Classification-of-Malware-WP-UPDATED.pdf "Quantum" Classification of Malware John Seymour Ph.D. student, University of Maryland, Baltimore County Quantum computation has recently become an important area for security research, with its applications to factoring large numbers and secure communication. In practice, only one company (D-Wave) has claimed to create a quantum computer which can solve relatively hard problems, and that claim has been met with much skepticism. Regardless of whether it is using quantum effects for computation or not, the D-Wave architecture cannot run the standard quantum algorithms, such as Grover’s and Shor’s. The D-Wave architecture is instead purported to be useful for machine learning and for heuristically solving NP-Complete problems. We'll show why the D-Wave and the machine learning problem for malware classification seem especially suited for each other. We also explain how to translate the classification problem for malicious executables into an optimization problem which a D-Wave machine can solve. Specifically, using a 512-qubit D-Wave Two processor, we show that a minimalist malware classifier, with cross-validation accuracy comparable to standard machine learning algorithms, can be created. However, even such a minimalist classifier incurs a surprising level of overhead. John Seymour is a Ph.D. student at the University of Maryland, Baltimore County, where he performs research at the intersection of machine learning and information security. He's mostly interested in avoiding and helping others avoid some of the major pitfalls in machine learning, especially in dataset preparation (seriously, do people still use malware datasets from 1998?) In 2014, he completed his Master’s thesis on the subject of quantum computation applied to malware analysis. He currently works at CyberPoint International, a company which performs network and host-based machine learning, located in Baltimore, MD.

In this classic GameTek segment Geoff talks about NP Complete problems in mathematics, and the ways that certain problems can be transformed into others. Duration: 06:00 (Yes, I know there was no GameTek Classic 35. Real Life got in the way a few weeks ago, and I decided to keep the numbering in sync with the Ludology numbering for simplicity)