Search results “Np complete problems cryptographic algorithms”

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Erik Demaine
In this lecture, Professor Demaine introduces NP-completeness.
License: Creative Commons BY-NC-SA
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Views: 97084
MIT OpenCourseWare

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Amartya Shankha Biswas
In this recitation, problems related to NP-Completeness are discussed.
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Views: 46815
MIT OpenCourseWare

Hackerdashery #2
Inspired by the Complexity Zoo wiki: https://complexityzoo.uwaterloo.ca/Complexity_Zoo
For more advanced reading, I highly recommend Scott Aaronson's blog, Shtetl-Optimized: http://www.scottaaronson.com/blog/
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Retro-fabulous, cabinet-sized computers:
System/360: http://en.wikipedia.org/wiki/IBM_System/360
photo: "360-91-panel". Licensed under Public domain via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:360-91-panel.jpg#mediaviewer/File:360-91-panel.jpg
PDP-8: http://en.wikipedia.org/wiki/PDP-8
photo: "PDP-8". Licensed under Public domain via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:PDP-8.jpg#mediaviewer/File:PDP-8.jpg
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Protein folding illustration: "Protein folding schematic" by Tomixdf (talk) - Own work (Original text: “self-made”). Licensed under Public domain via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Protein_folding_schematic.png#mediaviewer/File:Protein_folding_schematic.png
P vs. NP opinion poll: http://www.cs.umd.edu/~gasarch/papers/poll2012.pdf

Views: 1677588
hackerdashery

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Srinivas Devadas
In this lecture, Professor Devadas introduces approximation algorithms in the context of NP-hard problems.
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Views: 22629
MIT OpenCourseWare

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Erik Demaine
In this lecture, Professor Demaine introduces analysis techniques for data structures, and the implementation of algorithms based on this analysis.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 39006
MIT OpenCourseWare

This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.

Views: 6532
Udacity

PLEASE LIKE AND SUBSCRIBE

Views: 16003
University Academy- Formerly-IP University CSE/IT

This video is part of an online course, Intro to Theoretical Computer Science. Check out the course here: https://www.udacity.com/course/cs313.

Views: 1401
Udacity

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Erik Demaine
In this lecture, Professor Demaine tackles NP-hard problems using fixed-parameter algorithms.
License: Creative Commons BY-NC-SA
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Views: 14031
MIT OpenCourseWare

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Amartya Shankha Biswas
In this recitation, problems related to greedy algorithms are discussed.
License: Creative Commons BY-NC-SA
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Views: 26063
MIT OpenCourseWare

using probabilistic analysis to analyze the hiring problem

Views: 16762
Himmat Yadav

Russell Impagliazzo
Institute for Advanced Study
January 24, 2012
Abstract
The P vs. NP problem has sometimes been unofficially paraphrased as asking whether
it is possible to improve on exhaustive search for such problems as Satisfiability, Clique,
Graph Coloring, etc. However, known algorithms for each of these problems indeed are
substantially better than exhaustive search, if still exponential. Furthermore, although a
polynomial-time algorithm for any one of these problems implies one for all of them, these
improved exponential algorithms are highly specific, and it is unclear what the limit of
improvement should be.
In the past 15 years or so, a complexity theory for exponential complexities has emerged.
Fundamental to this theory are two related hypotheses: the Exponential Time Hypothesis,
that each k-SAT problem requires time 2ckn for 0 Time Hypothesis, that these constants tend towards 1 as k grows. Like the Unique Games
Conjecture for approximation algorithms, there is no consensus on whether these hypotheses
are true or false. However, also like UGC, there are many consequences both of their
truth and of their falsity. Either way gives a unified picture of the complexities of many
NP-complete problems. Furthermore, recent work has shown that ETH and SETH have implications
beyond exponential time algorithms, to parameterized complexity, cryptography,
data structures, and to the question of whether fundamental polynomial-time algorithms
can be further improved.
In this talk, I will discuss these hypotheses and their implications for complexity. We’ll see
how they can be used to get results about which problems in NP might require exponential
time, give evidence that some NP-complete problems are strictly harder than others, and
characterize the hard instances of NP-complete problems. We’ll touch on recent work by
Williams giving relationships between improved algorithms and circuit lower bounds. We’ll
show how to translate results from the exponential realm to reason that certain polynomial
time algorithms are unlikely to be improveable.
This is a survey of many results, only a fraction of which I am involved with, so I won’t
give a complete list of references. However, my work on this subject is joint with Ramamohan
Paturi and our students, Francis Zane, Chris Calabro and William Matthews.
For more videos, visit http://video.ias.edu

Views: 263
Institute for Advanced Study

Introduction to the most famous unsolved problem in Computer Science. Introduction to Turing Machines, runtime of algorithms, and the classes P and NP. What would the universe look like if P=NP. History of the problem, and attempts to solve the problem. Example adapted from https://en.wikipedia.org/wiki/Reduction_(complexity).

Views: 14365
CS50

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Amartya Shankha Biswas
In this recitation, problems related to approximation algorithms are discussed, namely the traveling salesman problem.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 46770
MIT OpenCourseWare

Kevin Leyton-Brown, University of British Columbia
https://simons.berkeley.edu/talks/kevin-leyton-brown-08-25-2016-1
Algorithms and Uncertainty Boot Camp

Views: 872
Simons Institute

This video is part of an online course, Intro to Theoretical Computer Science. Check out the course here: https://www.udacity.com/course/cs313.

Views: 2387
Udacity

NP-hard problems play an important role in cryptography and are frequent in graph theory. Large number of computers don’t reduce a complexity of an algorithm, but a solid architecture and design of distributed system can provide good scalability. In this talk, I will solve a non-standard NP-hard problem and will give examples how to solve it in a cluster of machines using Apache ZooKeeper.

Views: 554
jeeconf

This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.

Views: 8515
Udacity

This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.

Views: 555
Udacity

NP-hard-- Now suppose we found that A is reducible to B, then it means that B is at least as hard as A.
NP-Complete -- The group of problems which are both in NP and NP-hard are known as NP-Complete problem.

Views: 5194
HowTo

P vs. NP is one of the greatest unsolved problems. Just what is it, and why is it so important?
Created by: Cory Chang
Produced by: Vivian Liu
Script Editor: Justin Chen, Brandon Chen, Elaine Chang, Zachary Greenberg
Twitter: https://twitter.com/UBehavior
—
Extra Resources:
hackerdashery’s video: https://youtu.be/YX40hbAHx3s
Wiki: https://en.wikipedia.org/wiki/P_versus_NP_problem
Cook-Levin Theorem: https://en.wikipedia.org/wiki/Cook–Levin_theorem
SAT: https://en.wikipedia.org/wiki/Boolean_satisfiability_problem
P: https://en.wikipedia.org/wiki/P_(complexity)
NP: https://en.wikipedia.org/wiki/NP_(complexity)
EXPTIME: https://en.wikipedia.org/wiki/EXPTIME
NP-complete problems: https://en.wikipedia.org/wiki/List_of_NP-complete_problems
Picture Credits:
https://commons.wikimedia.org/wiki/File%3APyruvate_kinase_protein_domains.png: By Thomas Splettstoesser (www.scistyle.com) (Own work) [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons
https://cdn.vox-cdn.com/thumbor/PGO0kRZpyvRAFfhdAAJ73-w9e98=/0x25:680x408/1600x900/cdn.vox-cdn.com/uploads/chorus_image/image/42598430/deep-blue-kasparov.0.jpeg

Views: 72094
Undefined Behavior

In this video, I explain perhaps the most famous problem in all of Computer Science. Does P = NP? I define the terms and give examples of each. We also programmatically go through the traveling salesman problem. I experiment with a little bit of mixed reality in this video as well.
Code for this video:
https://github.com/llSourcell/p_vs_np_challenge
Nichole's winning code:
https://github.com/nhrigby
Mick's runner-up code:
https://github.com/mickvanhulst
Join the Wizard's Slack Channel:
https://wizards.herokuapp.com/
Some more great P vs NP resources:
https://danielmiessler.com/study/pvsnp/
https://qntm.org/pnp
http://news.mit.edu/2009/explainer-pnp
https://blog.codinghorror.com/the-girl-who-proved-p-np/
https://medium.com/the-physics-arxiv-blog/the-astounding-link-between-the-p-np-problem-and-the-quantum-nature-of-universe-7ef5eea6fd7a
Please subscribe! And like and comment and share. That's what keeps me going.
And please support me on Patreon!
https://www.patreon.com/user?u=3191693
I used the Tilt Brush mixed reality app to draw the complexity classes for fun. Thanks Az Balabanian and the Upload Collective for letting me shoot videos in VR! :
https://www.Azadux.com/mixed-reality
https://www.Uploadcollective.com
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w

Views: 131865
Siraj Raval

Views: 2808
Pavan Ravi

A visual explanation of p vs. np and the difference between polynomial vs exponential growth. This marks the end of the CS series! Support new content: https://www.patreon.com/artoftheproblem

Views: 34621
Art of the Problem

We introduce the topic of approximation algorithms by going over the K-Center Problem

Views: 20101
CSBreakdown

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Srinivas Devadas
In this lecture, Professor Devadas continues with cryptography, introducing encryption methods.
License: Creative Commons BY-NC-SA
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More courses at http://ocw.mit.edu

Views: 16400
MIT OpenCourseWare

Prof. Gideon illustrates the cryptographic aspects of complexity theory -- measuring computational burden for size increased problems.

Views: 1977
Gideon Samid

What is STRONG NP-COMPLETENESS? What does STRONG NP-COMPLETENESS mean? STRONG NP-COMPLETENESS meaning - STRONG NP-COMPLETENESS definition - STRONG NP-COMPLETENESS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
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In computational complexity, strong NP-completeness is a property of computational problems that is a special case of NP-completeness. A general computational problem may have numerical parameters. For example, the input to the bin packing problem is a list of objects of specific sizes and a size for the bins that must contain the objects—these object sizes and bin size are numerical parameters.
A problem is said to be strongly NP-complete (NP-complete in the strong sense), if it remains so even when all of its numerical parameters are bounded by a polynomial in the length of the input. A problem is said to be strongly NP-hard if a strongly NP-complete problem has a polynomial reduction to it; in combinatorial optimization, particularly, the phrase "strongly NP-hard" is reserved for problems that are not known to have a polynomial reduction to another strongly NP-complete problem.
Normally numerical parameters to a problem are given in positional notation, so a problem of input size n might contain parameters whose size is exponential in n. If we redefine the problem to have the parameters given in unary notation, then the parameters must be bounded by the input size. Thus strong NP-completeness or NP-hardness may also be defined as the NP-completeness or NP-hardness of this unary version of the problem.
For example, bin packing is strongly NP-complete while the 0-1 Knapsack problem is only weakly NP-complete. Thus the version of bin packing where the object and bin sizes are integers bounded by a polynomial remains NP-complete, while the corresponding version of the Knapsack problem can be solved in pseudo-polynomial time by dynamic programming.
While weakly NP-complete problems may admit efficient solutions in practice as long as their inputs are of relatively small magnitude, strongly NP-complete problems do not admit efficient solutions in these cases. From a theoretical perspective any strongly NP-hard optimization problem with a polynomially bounded objective function cannot have a fully polynomial-time approximation scheme (or FPTAS) unless P = NP. However, the converse fails: e.g. if P does not equal NP, knapsack with two constraints is not strongly NP-hard, but has no FPTAS even when the optimal objective is polynomially bounded.
Some strongly NP-complete problems may still be easy to solve on average, but it's more likely that difficult instances will be encountered in practice.

Views: 113
The Audiopedia

This video explains how to compute the RSA algorithm, including how to select values for d, e, n, p, q, and φ (phi).

Views: 198918
Anthony Vance

What is DETERMINISTIC ALGORITHM? What does DETERMINISTIC ALGORITHM mean? DETERMINISTIC ALGORITHM meaning - DETERMINISTIC ALGORITHM definition - DETERMINISTIC ALGORITHM explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
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In computer science, a deterministic algorithm is an algorithm which, given a particular input, will always produce the same output, with the underlying machine always passing through the same sequence of states. Deterministic algorithms are by far the most studied and familiar kind of algorithm, as well as one of the most practical, since they can be run on real machines efficiently.
Formally, a deterministic algorithm computes a mathematical function; a function has a unique value for any input in its domain, and the algorithm is a process that produces this particular value as output.
Deterministic algorithms can be defined in terms of a state machine: a state describes what a machine is doing at a particular instant in time. State machines pass in a discrete manner from one state to another. Just after we enter the input, the machine is in its initial state or start state. If the machine is deterministic, this means that from this point onwards, its current state determines what its next state will be; its course through the set of states is predetermined. Note that a machine can be deterministic and still never stop or finish, and therefore fail to deliver a result.
Examples of particular abstract machines which are deterministic include the deterministic Turing machine and deterministic finite automaton.
A variety of factors can cause an algorithm to behave in a way which is not deterministic, or non-deterministic:
If it uses external state other than the input, such as user input, a global variable, a hardware timer value, a random value, or stored disk data.
If it operates in a way that is timing-sensitive, for example if it has multiple processors writing to the same data at the same time. In this case, the precise order in which each processor writes its data will affect the result.
If a hardware error causes its state to change in an unexpected way.
Although real programs are rarely purely deterministic, it is easier for humans as well as other programs to reason about programs that are. For this reason, most programming languages and especially functional programming languages make an effort to prevent the above events from happening except under controlled conditions.
The prevalence of multi-core processors has resulted in a surge of interest in determinism in parallel programming and challenges of non-determinism have been well documented. A number of tools to help deal with the challenges have been proposed to deal with deadlocks and race conditions.
It is advantageous, in some cases, for a program to exhibit nondeterministic behavior. The behavior of a card shuffling program used in a game of blackjack, for example, should not be predictable by players — even if the source code of the program is visible. The use of a pseudorandom number generator is often not sufficient to ensure that players are unable to predict the outcome of a shuffle. A clever gambler might guess precisely the numbers the generator will choose and so determine the entire contents of the deck ahead of time, allowing him to cheat; for example, the Software Security Group at Reliable Software Technologies was able to do this for an implementation of Texas Hold 'em Poker that is distributed by ASF Software, Inc, allowing them to consistently predict the outcome of hands ahead of time. These problems can be avoided, in part, through the use of a cryptographically secure pseudo-random number generator, but it is still necessary for an unpredictable random seed to be used to initialize the generator. For this purpose a source of nondeterminism is required, such as that provided by a hardware random number generator.
Note that a negative answer to the P=NP problem would not imply that programs with nondeterministic output are theoretically more powerful than those with deterministic output. The complexity class NP (complexity) can be defined without any reference to nondeterminism using the verifier-based definition.

Views: 1708
The Audiopedia

Randomised Computation, by Daria Dicu
Abstract: Randomised algorithms are the simplest and fastest known solution to many decision problems. We reason about randomised algorithms using the concept of probabilistic Turing machines, which are a variant of nondeterministic Turing machines that have probabilistic transition choice.
This talk is aimed as a discussion around the various complexity classes associated with randomised computation (BPP, RP, ZPP ). I shall start by presenting these classes, alongside known relationships between them and complexity classes studied in the Part IB course (P, NP).
We will then see how randomised algorithms provide much more efficient solutions than deterministic ones by looking at the Schwartz-Zippel lemma applied to Polynomial Identity Testing, which gives a polynomial time Monte Carlo algorithm, as opposed to its deterministic counterpart, which is exponential.
In conclusion, I shall discuss the open problem of P = BPP and the usage of pseudorandom number generators to deterministically simulate randomised algorithms.

Views: 1122
Churchill CompSci Talks

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Erik Demaine
In this lecture, Professor Demaine introduces cache-oblivious algorithms.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 8586
MIT OpenCourseWare

Find Complete Code at GeeksforGeeks Article: http://www.geeksforgeeks.org/searching-for-patterns-set-3-rabin-karp-algorithm/
This video is contributed by Illuminati
Please Like, Comment and Share the Video among your friends.
Also, Subscribe if you haven't already! :)

Views: 54203
GeeksforGeeks

Learn more on http://www.science4all.org about:
P versus NP: http://www.science4all.org/le-nguyen-hoang/pnp/
Divide and Conquer: http://www.science4all.org/le-nguyen-hoang/divide-and-conquer/
Probabilistic Algorithms: http://www.science4all.org/le-nguyen-hoang/probabilistic-algorithms/
Cryptography and Number Theory: http://www.science4all.org/scottmckinney/cryptography-and-number-theory/
By Lê Nguyên Hoang,
Not an Ordinary Seminar, GERAD.
For one hour, I will take you through some of the most amazing recent subfields of mathematics. From computational theory to chaos theory, from infinity to ergodicity, from mathematical physics to category theory, we will be unveiling mind-blowing results of modern mathematics. Although primarily aimed at non-mathematicians, it should be of great interest to everyone.

Views: 1489
Science4All (english)

What is HAMILTONIAN COMPLETION? What does HAMILTONIAN COMPLETION mean? HAMILTONIAN COMPLETION meaning - HAMILTONIAN COMPLETION definition - HAMILTONIAN COMPLETION explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
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The Hamiltonian completion problem is to find the minimal number of edges to add to a graph to make it Hamiltonian.
The problem is clearly NP-hard in general case (since its solution gives an answer to the NP-complete problem of determining whether a given graph has a Hamiltonian cycle). The associated decision problem of determining whether K edges can be added to a given graph to produce a Hamiltonian graph is NP-complete.
Moreover, Hamiltonian completion belongs to the APX complexity class, i.e., it is unlikely that efficient constant ratio approximation algorithms exist for this problem.
The problem may be solved in polynomial time for certain classes of graphs, including series-parallel graphs and their generalizations, which include outerplanar graphs, as well as for a line graph of a tree or a cactus graph.
Gamarnik et al. use a linear time algorithm for solving the problem on trees to study the asymptotic number of edges that must be added for sparse random graphs to make them Hamiltonian.

Views: 53
The Audiopedia

In this video I provide the depth understanding about the Probabilistic and Randomized Algorithm topic with definitions, descriptions and examples.

Views: 246
Rajvi Trivedi

This video is part of an online course, Intro to Theoretical Computer Science. Check out the course here: https://www.udacity.com/course/cs313.

Views: 195
Udacity

Debunking the subtle differences between the two very similar program runtimes, and highlighting why this distinction is so important.

Views: 7716
CSBreakdown

The first question we computer scientists ask when facing a new algorithmic challenge is: is it NP-hard, or is it in P? Surprisingly, for many important problems, the answer is "neither!" I will discuss recent progress towards understanding the complexity of those problems.
See more on this video at https://www.microsoft.com/en-us/research/video/hardness-approximation-p-np/

Views: 642
Microsoft Research

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Erik Demaine
In this lecture, Professor Demaine reviews hashing in the context of randomized algorithms.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 25597
MIT OpenCourseWare

Avi Wigderson
Institute for Advanced Study
October 24, 2008
The "P vs. NP" problem is a central outstanding problem of computer science and mathematics. In this talk, Professor Wigderson attempts to describe its technical, scientific, and philosophical content, its status, and the implications of its two possible resolutions.
More videos on http://video.ias.edu

Views: 1170
Institute for Advanced Study

Subject:Computer Science
Paper: Design and analysis of algorithms

Views: 1088
Vidya-mitra

Part 17: This video might be a bit more boring reversing, and I even failed to recognise the implemented algorithm.
🌴 Playlist: https://www.youtube.com/playlist?list=PLhixgUqwRTjzzBeFSHXrw9DnQtssdAwgG
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Views: 28871
LiveOverflow

for the part that mentions:
“links in the description:”
https://www.youtube.com/watch?v=YX40hbAHx3s
Sources also listed in video:
References
[1] Pomerance, Carl, Selfridge, and Samuel Wagstaff, Jr. "The Pseudoprimes."
Mathematics of Computation 35.151 (1980): 1094-024. American
Mathematical Society. Web.
[2] "Baillie–PSW Primality Test." Wikipedia. Wikimedia Foundation, 31 Mar. 2017. Web.
07 Apr. 2017.
[3] Nicely, Thomas R. "The Baillie-PSW Primality Test." Trnicely.net. N.p., 10 June 2005.
Web.
[4] Wolfram Alpha
[5] https://wiki.python.org/moin/TimeComplexity
[6] https://ivokoller.com/rsa-part-3/
Media Sources
Cormac. "Boston Irish." October 2012. N.p., 25 Oct. 2012. Web. 23 Apr. 2017.
Schnickledooger. "Schnickledooger." Someone Please Help the Skywalker Family. N.p., 02 July 2014. Web. 23 Apr. 2017.
"New Books." Introduction to Cryptography. N.p., n.d. Web. 23 Apr. 2017.
Pinterest
"Night Photos." Free Stock Photos. N.p., n.d. Web. 23 Apr. 2017.
http://l4wisdom.com/python/python_list.php
http://stackoverflow.com/questions/111307/whats-p-np-and-why-is-it-such-a-famous-question
http://demonstrations.wolfram.com/Pseudoprime/
https://en.wikipedia.org/wiki/Binary_search_algorithm#/media/File:Binary_Search_Depiction.svg
Python images - https://www.youtube.com/watch?v=HfzCpDilEVM
https://en.wikipedia.org/wiki/Lucas–Lehmer_primality_test
https://ivokoller.com/rsa-part-3/
https://www.businesscomputingworld.co.uk/5-things-your-files-would-tell-you-if-they-could-talk/
oh no face - http://wazzuptonight.com/never-lose-car-again/oh-no1/
https://en.wikipedia.org/wiki/RSA_(cryptosystem)
The trump - https://www.youtube.com/watch?v=EEA33bAXyNM
background Music :
https://www.youtube.com/watch?v=L5oNESifgpk
https://www.youtube.com/watch?v=tUgUqlm9nzo

Views: 179
evlynn hofbauer

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Erik Demaine
In this lecture, Professor Demaine covers different algorithmic solutions for the All-Pairs Shortest Paths problem.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 36587
MIT OpenCourseWare

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Nancy Ann Lynch
In this lecture, Professor Lynch introduces synchronous distributed algorithms.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 9818
MIT OpenCourseWare

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Srinivas Devadas
In this lecture, Professor Devadas introduces randomized algorithms, looking at solving sorting problems with this new tool.
License: Creative Commons BY-NC-SA
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