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Introduction to combinations and permutations
Learn more: http://www.khanacademy.org/video?v=8TIben0bJpU A different way to think about the probability of getting 2 heads in 4 flips.
Much of enumerative combinatorics concerns the question: "Count the number a_n of elements of a set S_n for n=1,2,..." We discuss four types of answers: an e...
Subscribe Now: http://www.youtube.com/subscription_center?add_user=Ehow Watch More: http://www.youtube.com/Ehow Combinatorics is a very important course in t...
Combinatorics with Day[9]: Bijections.
Combinations and Permutations can be among the hardest of GMAT math concepts. Generally, these rarely-tested concepts aren't worth focusing on until you've m...
A look at Venn Diagrams and the Inclusion-Exclusion Principle. Includes the solution to a question from Richard G. Brown's "Advanced Mathematics: Precalculus...
In this lecture we will discuss a different type of combinations - the combinations with repetitions. Assume, again, that we have to pick K objects out of a set of N different objects, but after each pick we just record which object we picked and then put it back, so we can pick the same object again repetitively. Now we can have the number of picks K not restricted by the number of objects N, it can be smaller, equal or greater than N. Another, more practical situation of combinations with repetitions can be observed if the number N represents not the total number of objects, but the number of different types of objects with an unlimited number of objects of each type (so, we don't have to return back our pick to facilitate the repetition). Now our task is to pick K objects, and combinations we pick differ only by their composition, that is the number of objects of each type. For example, we came to a store to buy some wine. We would like to pick the total of K=5 bottles and we have N=3 types of wine we want - red, white and sparkling. The question is, how many different combinations of these three types of wine we can choose in a set of five bottles, assuming the store has sufficient supply of each type. As in the case of regular combinations, let's come up with some clever logical procedure to resolve this problem. We will use the wine example above to illustrate it. Let's use a letter W to indicate a bottle of wine and a slash / to separate wines of different types. For instance, we picked 1 red, 1 white and 3 sparkling wines. Then we can form a string that represents our purchase as follows: W/W/WWW Analogously, string /WWWWW/ indicates our pick of five bottles of white wine, string //WWWWW indicates a purchase of five bottles of sparkling wine, string WW/WW/W indicates a purchase of two red, two white and one sparking wine bottles. As we see, our pick is fully defined by these strings and the total number of different combinations is the total number of different strings of seven characters with five W's and two slashes. Since the difference between strings is only in the position of two slashes among seven characters, each string can be obtained by just picking two different numbers out of numbers from 1 to 7 and call the characters with these numbers "a type separator - slash", while the characters at other positions will be called "wine bottle - W". Therefore, the total number of our combinations with repetitions equals to a number of regular combinations of 2 objects of a set of 7 different objects: C(7,2) = 7! / (2!·5!) = 21. Incidentally, instead of picking the positions of two slashes, we can equally say that our pick is fully defined by the positions of five characters W. That leads to a number of regular combinations of five objects out of a set of seven different objects with a formula C(7,2) = 7! / (5!·2!) = 21. This is exactly the same as above, which should be of no surprise for us since the formula for regular combinations is symmetrical relative to a subset of chosen objects and a subset of remaining ones. Let's generalize this logic. We have N types of objects and unlimited number of objects of each type. We pick K objects out of them. How many different combinations of types (compositions of our set of picked objects) can be picked? Following the same logic and symbolics, let's imagine strings containing K characters W signifying a picked objects and N−1 type separators - slashes /. The length of each string is K+N−1. Each such string represents a particular composition of a set of picked objects by their types. So, the position of slashes (or, as we mentioned above, the position of W's) fully define the composition of a set of picked objects. The number of different strings of this kind is the number of regular combinations of N−1 objects out of K+N−1 or, equally, the number of regular combinations of K objects out of K+N−1: C(K+N−1,N−1) = = C(K+N−1,K) = = (K+N−1)! / [K!·(N−1)!] Let's consider a couple of simple examples to check this formula. Example 1: The number of object types N equals to 1. In this case we have only one choice to pick K objects - all objects are of that one and only type. The formula gives (K+1−1)! / [K!·(1−1)!] = = K! / (K!·0!) = 1 as is supposed to be. Example 2: The number of object types N equals to 2. In this case we can pick K objects by choosing 0 objects of the first type and K objects of the second type or 1 object of the first type and K−1 objects of the second type etc. up to K objects of the first type and 0 objects of the second type. The number of choices is K+1. The formula gives (K+2−1)! / [K!·(2−1)!] = = (K+1)! / [K!·1!] = = (K+1)! / K! = K+1 as is supposed to be.
http://www.manhattanreview.com/gmat-online/. This video covers the permutation and combinatorics problems in the GMAT Math Problem Solving section. Manhattan...
http://nitreg.com How do you figure out combinatorics in poker? Watch this video and find out. The math is fairly easy and I show you a couple of examples to...
Problem 1 What is the number of partial permutations of N given objects by K objects under a restriction that each such partial permutation must contain X pa...
Problem 1 A poker dealer gave you five cards from the standard deck of 52 cards. How many different "Four of a Kind" combinations you can get? The combination "Four of a Kind" is the one when you have four cards of the same rank (say, four 9's or four Kings) among five cards in your hand. Answer: 624 Problem 2 A poker dealer gave you five cards from the standard deck of 52 cards. How many different "Full House" combinations you can get? The combination "Full House" is the one when you have three cards of one rank (say, three 8's or three Jacks) and two cards of another rank (say, two queens or two 5's) among five cards in your hand. Answer: 3744 Problem 3 A poker dealer gave you five cards from the standard deck of 52 cards. How many different "Straight" combinations you can get? A "Straight" is a combination of five cards with sequential ranks of not of the same suit. For example, Queen, Jack, 10, 9, 8 of not of the same suit. If they are of the same suit, the combination is called "Straight Flush" and is considered to be a different combination. There is one more rule for a "Straight". The ace of any suit can be the highest ranking card in a deck for a "Straight" combination Ace, King, Queen, Jack, 10 or the lowest ranking card in a deck with the rank of 1 in a combination 5, 4, 3, 2, A. Answer: 10,200 Problem 4 A poker dealer gave you five cards from the standard deck of 52 cards. How many different "Flush" combinations you can get? A "Flush" is a combination of five cards of the same suit with ranks not sequentially following each other (then it would be "Straight Flush" combination, which is different from "Flush"). Answer: 5,108 Problem 5 A poker dealer gave you five cards from the standard deck of 52 cards. How many different "Three of a Kind" combinations you can get? The combination "Three of a Kind" is the one when you have three cards of the same rank (say, three 9's or three Kings) among five cards in your hand, and the other two cards of not of this rank (otherwise, you would have "Four of a Kind" combination) and not of the same rank among themselves (otherwise, you would have "Full House" combination). Answer: 54,912
Alexander Postnikov (MIT) The Combinatorics of the Grassmanian I
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This is part 1 of learning basic counting and combinations in discrete mathematics. I will give some examples to get you introduced to the idea of finding combinations.
A more advanced application of combinations: we use simple combinatorics to count the number of ways to travel from one place to another and to count the number of small rectangles inside a larger rectangle. An advanced tutorial that I'm sure you'll like.
Combinatorics is a branch of mathematics concerning the study of finite or countable discrete structures. Aspects of combinatorics include counting the structures of a given kind and size (enumerat...
Poker Plays You Can Use is a book written by Doug Hull that has an appendix that covers combinatorics you can do at the poker table.
Entity walks through how to quickly and accurately count combinations of hands. This skill will help you understand what hands your opponent is likely to hol...
Combinatorics is a much newer part of mathematics than such classical subjects as Geometry or Algebra. Very important stimulus to its development was the Theory of Probabilities, which is the next subject in this course. Later on, Game Theory and contemporary Computer Science were the other fields of application of the combinatorics. The subject of Permutations is calculating the number of possibilities of putting certain number of objects in certain order. Examples are numerous. For instance, you have to visit 3 different places A, B and C. The order in which you visit them can be ABC, ACB, BAC, BCA, CAB and CBA. Actually, these 6 different ways to visit 3 places are the only possible ones, there are no other ways of putting them in the order of visiting. In many cases it's very important to know how many possibilities to do something like this visiting exists. That's the task of calculating the permutations. The simplest form of permutations is putting N objects in some order. For example, we have numbers from 1 to N and want to write them down in some order. We can write them in ascending order, in descending order or in many other types of order. But what is the number of different ways we can write them down? Ordering of N objects assumes that we have to choose the object #1, then, among other objects we have to choose the object #2, then among whatever is left we have to choose the object #3 etc. Continuing this process to the end, we finally get to object #N. For #1 object we have N candidates. With each of them there remain N−1 candidates for #2 spot. So, we have N·(N−1) choices for the first 2 places. With each of them there remain N−2 candidates for #3 spot, which brings the total number of variants for the first 3 places to N·(N−1)·(N−2). Continuing this process to all N places, we come up with the total number of variants to position N objects in some order equal to N·(N−1)·(N−2)·...·2·1. This quantity is usually written in the form N! and pronounced "N factorial". At this point we'd like to suggest certain criticism to many textbooks that explain this more or less in the same way. The explanation above is just the explanation, not a proof. So, to stop at this point, as many textbooks do, cannot be considered a truly mathematical approach. We have to prove it, and that's exactly what we are going to do next. Proof We are going to prove by induction that the number of permutations of N different objects equals to N·(N−1)·(N−2)·...·2·1 = N! (that is, N factorial) Induction step 1. The formula is obviously correct for N=1 because it produces the result 1 and there is only one way to position a single object in some order. Induction step 2. Assume that the formula is correct for N=K, that is that the number of permutations of K objects is K!. Induction step 3. Consider we have N=K+1 objects. Each permutation (the way of ordering) of these objects involves positioning of the first K objects in some way (and there are K! of these ways, as we assumed on step 2) and placing the (K+1)th object somewhere among these first K objects. The (K+1)th object can stand before the first, between the first and the second, between the second and the third,..., between (K−1)th and Kth objects and, finally, after the Kth object. This constitutes K+1 different positions for the (K+1)th object. Therefore, for any permutation of the first K objects there are K+1 different permutations of the whole set of K+1 objects. Hence, the total number of permutations of K+1 objects equals to K! multiplied by (K+1). But K! · (K+1) = (K+1)!, as follows from the definition of the "factorial" as a product of all natural numbers from 1 to a given number. That proves that the formula retains its form when we move from N=K objects to N=K+1 objects. This completes the proof. We will use the symbol P(N) to designate the number of permutations of N objects. We, therefore, have proved that P(N) = N!
Extremal and Probabilistic Combinatorics In this course we will introduce the student to the basic theorems and proof techniques in extremal graph theory and probabilistic combinatorics. We shall emphasize the close links between these two areas, and provide the background material for modern research fields such as additive combinatorics, monotone and hereditary properties, and graph limits. We also discuss some simple but powerful applications of techniques from functional analysis and linear algebra. The course has no prerequisites. Programa: 1. Ramsey Theory: Finite and infinite versions. Erdös' random proof of the lower bound. Van der Waarden's Theorem. Statement of Szemerédi's Theorem. 2. Extremal Graph Theory: The theorems of Turán, Erdös-Stone and Kovari-Sós-Turán. 3. The Erdös-Renyi Random Graph: Graphs with high girth and chromatic number. Extremal number of C2k. 1st and 2nd moment methods. Janson's inequality. The giant component. 4. Analytic and Algebraic Methods: The Kneser graph and the Borsuk-Ulam Theorem. The Frankl-Wilson inequality and Borsuk's Conjecture. 5. The Szemerédi Regularity Lemma: Statement and applications, e.g., proof of Erdös-Stone, Erdös-Frankl-Rödl. Proof of Roth's Theorem via the triangle-removal lemma. 6. Dependent Random Choice: Applications, including the proof of the Balog-Szemerédi-Gowers Theorem. Robert Morris
(Source: ... Brown's research areas include number theory, cryptography, combinatorics, and the history of mathematics ... D ... C ... vt. edu.
noodls 2015-04-02The branch of math used to solve the problem is known as combinatorial probability, or ...
Forbes 2015-03-13His research focuses on geometric combinatorics, the study of geometric objects and their ...
noodls 2015-03-13... in probabilistic and extremal combinatorics (becoming a Fellow of the Royal Society in the process).
The Guardian 2015-03-11... and combinatorics - a branch of mathematics concerning the study of finite or countable structures.
noodls 2015-03-09R for Biologists (BIOL) Fundamentals for Polyhedral Combinatorics (MATH) Children's Rights (Landon ...
noodls 2015-02-24... and estimation, the design of sample surveys, and elementary applied probability and combinatorics.
noodls 2015-02-24Bukh (right), an assistant professor in the Mathematical Sciences Department, studies the interface ...
noodls 2015-02-23... of Boston's Northeastern University, whose research in combinatorics is supported by an NSA grant.
South China Morning Post 2015-02-12(Source: ... He received his PhD through the Algorithms, Combinatorics and Optimization Program at Carnegie Mellon University ... :03
noodls 2015-02-04... geometry for general relativityDiscrete mathematics and combinatorics for analysis of algorithms.
Medium 2015-02-03Similarly, poetry and music inspired the creation of combinatorics, the art or science of combining ...
The Times of India 2015-01-24Bengaluru: ... Poetry, he said, has given rise to some of mathematics' interesting discoveries like 'combinatorics'.
The Times of India 2015-01-21Combinatorics is a branch of mathematics concerning the study of finite or countable discrete structures. Aspects of combinatorics include counting the structures of a given kind and size (enumerative combinatorics), deciding when certain criteria can be met, and constructing and analyzing objects meeting the criteria (as in combinatorial designs and matroid theory), finding "largest", "smallest", or "optimal" objects (extremal combinatorics and combinatorial optimization), and studying combinatorial structures arising in an algebraic context, or applying algebraic techniques to combinatorial problems (algebraic combinatorics).
Combinatorial problems arise in many areas of pure mathematics, notably in algebra, probability theory, topology, and geometry, and combinatorics also has many applications in optimization, computer science, ergodic theory and statistical physics. Many combinatorial questions have historically been considered in isolation, giving an ad hoc solution to a problem arising in some mathematical context. In the later twentieth century, however, powerful and general theoretical methods were developed, making combinatorics into an independent branch of mathematics in its own right. One of the oldest and most accessible parts of combinatorics is graph theory, which also has numerous natural connections to other areas. Combinatorics is used frequently in computer science to obtain formulas and estimates in the analysis of algorithms.