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.
Learn more: http://www.khanacademy.org/video?v=8TIben0bJpU A different way to think about the probability of getting 2 heads in 4 flips.
41:01
MathHistory29: Combinatorics
MathHistory29: Combinatorics
MathHistory29: Combinatorics
We give a brief historical introduction to the vibrant modern theory of combinatorics, concentrating on examples coming from counting problems, graph theory and generating functions. In particular we look at partitions and Euler's pentagonal theorem, Fibonacci numbers, the Catalan sequence, the Erdos Szekeres theorem, Ramsey theory and the Kirkman Schoolgirls problem.
1:38
Introduction to Combinatorics : Principles of Math
Introduction to Combinatorics : Principles of Math
Introduction to Combinatorics : Principles of Math
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...
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...
12:49
Algebra II: binomial Expansion and Combinatorics
Algebra II: binomial Expansion and Combinatorics
Algebra II: binomial Expansion and Combinatorics
65 (done another way) - 66, combinatorics and binomial expansions
47:42
Extremal and Probabilistic Combinatorics Aula 1 Parte 1
Extremal and Probabilistic Combinatorics Aula 1 Parte 1
Extremal and Probabilistic Combinatorics Aula 1 Parte 1
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 de
10:23
Counting and Combinatorics in Discrete Math Part 1
Counting and Combinatorics in Discrete Math Part 1
Counting and Combinatorics in Discrete Math Part 1
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.
http://www.manhattanreview.com/gmat-online/. This video covers the permutation and combinatorics problems in the GMAT Math Problem Solving section. Manhattan...
20:31
How To Do Combinatorics In Poker
How To Do Combinatorics In Poker
How To Do Combinatorics In Poker
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...
18:10
Unizor - Combinatorics - Permutations
Unizor - Combinatorics - Permutations
Unizor - Combinatorics - Permutations
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 differen
89:36
Alexander Postnikov (MIT) The Combinatorics of the Grassmanian I
Alexander Postnikov (MIT) The Combinatorics of the Grassmanian I
Alexander Postnikov (MIT) The Combinatorics of the Grassmanian I
Alexander Postnikov (MIT)
The Combinatorics of the Grassmanian I
Learn more: http://www.khanacademy.org/video?v=8TIben0bJpU A different way to think about the probability of getting 2 heads in 4 flips.
41:01
MathHistory29: Combinatorics
MathHistory29: Combinatorics
MathHistory29: Combinatorics
We give a brief historical introduction to the vibrant modern theory of combinatorics, concentrating on examples coming from counting problems, graph theory and generating functions. In particular we look at partitions and Euler's pentagonal theorem, Fibonacci numbers, the Catalan sequence, the Erdos Szekeres theorem, Ramsey theory and the Kirkman Schoolgirls problem.
1:38
Introduction to Combinatorics : Principles of Math
Introduction to Combinatorics : Principles of Math
Introduction to Combinatorics : Principles of Math
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...
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...
12:49
Algebra II: binomial Expansion and Combinatorics
Algebra II: binomial Expansion and Combinatorics
Algebra II: binomial Expansion and Combinatorics
65 (done another way) - 66, combinatorics and binomial expansions
47:42
Extremal and Probabilistic Combinatorics Aula 1 Parte 1
Extremal and Probabilistic Combinatorics Aula 1 Parte 1
Extremal and Probabilistic Combinatorics Aula 1 Parte 1
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 de
10:23
Counting and Combinatorics in Discrete Math Part 1
Counting and Combinatorics in Discrete Math Part 1
Counting and Combinatorics in Discrete Math Part 1
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.
http://www.manhattanreview.com/gmat-online/. This video covers the permutation and combinatorics problems in the GMAT Math Problem Solving section. Manhattan...
20:31
How To Do Combinatorics In Poker
How To Do Combinatorics In Poker
How To Do Combinatorics In Poker
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...
18:10
Unizor - Combinatorics - Permutations
Unizor - Combinatorics - Permutations
Unizor - Combinatorics - Permutations
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 differen
89:36
Alexander Postnikov (MIT) The Combinatorics of the Grassmanian I
Alexander Postnikov (MIT) The Combinatorics of the Grassmanian I
Alexander Postnikov (MIT) The Combinatorics of the Grassmanian I
Alexander Postnikov (MIT)
The Combinatorics of the Grassmanian I
11:17
Combination formula
Combination formula
Combination formula
5:15
Die rolling probability | Probability and combinatorics | Precalculus | Khan Academy
Die rolling probability | Probability and combinatorics | Precalculus | Khan Academy
Die rolling probability | Probability and combinatorics | Precalculus | Khan Academy
We're thinking about the probability of rolling doubles on a pair of dice. Let's create a grid of all possible outcomes.
Watch the next lesson: https://www.khanacademy.org/math/precalculus/prob_comb/independent_events_precalc/v/lebron-asks-about-the-chances-of-making-10-free-throws?utm_source=YT&utm;_medium=Desc&utm;_campaign=Precalculus
Missed the previous lesson?
https://www.khanacademy.org/math/precalculus/prob_comb/independent_events_precalc/v/getting-at-least-one-heads?utm_source=YT&utm;_medium=Desc&utm;_campaign=Precalculus
Precalculus on Khan Academy: You may think that precalculus is simply the course you take before calculus. You wou
3:56
Simple Combinatorics
Simple Combinatorics
Simple Combinatorics
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...
13:12
Combinatorics: Venn Diagrams and the Inclusion-Exclusion Principle
Combinatorics: Venn Diagrams and the Inclusion-Exclusion Principle
Combinatorics: Venn Diagrams and the Inclusion-Exclusion Principle
A look at Venn Diagrams and the Inclusion-Exclusion Principle. Includes the solution to a question from Richard G. Brown's "Advanced Mathematics: Precalculus...
29:16
0.4 Analytic Combinatorics 2915)
0.4 Analytic Combinatorics 2915)
0.4 Analytic Combinatorics 2915)
26:47
Basic Combinatorics
Basic Combinatorics
Basic Combinatorics
I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
15:08
Probability using Combinatorics
Probability using Combinatorics
Probability using Combinatorics
8:28
Combinatorics Problems
Combinatorics Problems
Combinatorics Problems
1:02
Introduction to Analytic Combinatorics, Part I with Robert Sedgewick
Introduction to Analytic Combinatorics, Part I with Robert Sedgewick
Introduction to Analytic Combinatorics, Part I with Robert Sedgewick
The course "Introduction to Analytic Combinatorics, Part I" by Professor Robert Sedgewick from Princeton University, will be offered free of charge to everyo...
3:45
Permutations and Combinations 1
Permutations and Combinations 1
Permutations and Combinations 1
U12_L2_T3_we1 Permutations and Combinations 1 More free lessons at: http://www.khanacademy.org/video?v=oQpKtm5TtxU Content provided by TheNROCproject.org - (...
We give a brief historical introduction to the vibrant modern theory of combinatorics, concentrating on examples coming from counting problems, graph theory and generating functions. In particular we look at partitions and Euler's pentagonal theorem, Fibonacci numbers, the Catalan sequence, the Erdos Szekeres theorem, Ramsey theory and the Kirkman Schoolgirls problem.
We give a brief historical introduction to the vibrant modern theory of combinatorics, concentrating on examples coming from counting problems, graph theory and generating functions. In particular we look at partitions and Euler's pentagonal theorem, Fibonacci numbers, the Catalan sequence, the Erdos Szekeres theorem, Ramsey theory and the Kirkman Schoolgirls problem.
published:03 Jun 2015
views:115
Introduction to Combinatorics : Principles of Math
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...
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...
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...
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...
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
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
published:03 Feb 2015
views:3
Counting and Combinatorics in Discrete Math Part 1
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.
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.
http://www.manhattanreview.com/gmat-online/. This video covers the permutation and combinatorics problems in the GMAT Math Problem Solving section. Manhattan...
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...
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...
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!
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!
published:12 May 2014
views:1
Alexander Postnikov (MIT) The Combinatorics of the Grassmanian I
We're thinking about the probability of rolling doubles on a pair of dice. Let's create a grid of all possible outcomes.
Watch the next lesson: https://www.khanacademy.org/math/precalculus/prob_comb/independent_events_precalc/v/lebron-asks-about-the-chances-of-making-10-free-throws?utm_source=YT&utm;_medium=Desc&utm;_campaign=Precalculus
Missed the previous lesson?
https://www.khanacademy.org/math/precalculus/prob_comb/independent_events_precalc/v/getting-at-least-one-heads?utm_source=YT&utm;_medium=Desc&utm;_campaign=Precalculus
Precalculus on Khan Academy: You may think that precalculus is simply the course you take before calculus. You would be right, of course, but that definition doesn't mean anything unless you have some knowledge of what calculus is. Let's keep it simple, shall we? Calculus is a conceptual framework which provides systematic techniques for solving problems. These problems are appropriately applicable to analytic geometry and algebra. Therefore....precalculus gives you the background for the mathematical concepts, problems, issues and techniques that appear in calculus, including trigonometry, functions, complex numbers, vectors, matrices, and others. There you have it ladies and gentlemen....an introduction to precalculus!
About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Precalculus channel:
https://www.youtube.com/channel/UCBeHztHRWuVvnlwm20u2hNA?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
We're thinking about the probability of rolling doubles on a pair of dice. Let's create a grid of all possible outcomes.
Watch the next lesson: https://www.khanacademy.org/math/precalculus/prob_comb/independent_events_precalc/v/lebron-asks-about-the-chances-of-making-10-free-throws?utm_source=YT&utm;_medium=Desc&utm;_campaign=Precalculus
Missed the previous lesson?
https://www.khanacademy.org/math/precalculus/prob_comb/independent_events_precalc/v/getting-at-least-one-heads?utm_source=YT&utm;_medium=Desc&utm;_campaign=Precalculus
Precalculus on Khan Academy: You may think that precalculus is simply the course you take before calculus. You would be right, of course, but that definition doesn't mean anything unless you have some knowledge of what calculus is. Let's keep it simple, shall we? Calculus is a conceptual framework which provides systematic techniques for solving problems. These problems are appropriately applicable to analytic geometry and algebra. Therefore....precalculus gives you the background for the mathematical concepts, problems, issues and techniques that appear in calculus, including trigonometry, functions, complex numbers, vectors, matrices, and others. There you have it ladies and gentlemen....an introduction to precalculus!
About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Precalculus channel:
https://www.youtube.com/channel/UCBeHztHRWuVvnlwm20u2hNA?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
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...
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...
A look at Venn Diagrams and the Inclusion-Exclusion Principle. Includes the solution to a question from Richard G. Brown's "Advanced Mathematics: Precalculus...
A look at Venn Diagrams and the Inclusion-Exclusion Principle. Includes the solution to a question from Richard G. Brown's "Advanced Mathematics: Precalculus...
The course "Introduction to Analytic Combinatorics, Part I" by Professor Robert Sedgewick from Princeton University, will be offered free of charge to everyo...
The course "Introduction to Analytic Combinatorics, Part I" by Professor Robert Sedgewick from Princeton University, will be offered free of charge to everyo...
Learn more: http://www.khanacademy.org/video?v=8TIben0bJpU A different way to think about the probability of getting 2 heads in 4 flips.
41:01
MathHistory29: Combinatorics
We give a brief historical introduction to the vibrant modern theory of combinatorics, con...
published:03 Jun 2015
MathHistory29: Combinatorics
MathHistory29: Combinatorics
published:03 Jun 2015
views:115
We give a brief historical introduction to the vibrant modern theory of combinatorics, concentrating on examples coming from counting problems, graph theory and generating functions. In particular we look at partitions and Euler's pentagonal theorem, Fibonacci numbers, the Catalan sequence, the Erdos Szekeres theorem, Ramsey theory and the Kirkman Schoolgirls problem.
1:38
Introduction to Combinatorics : Principles of Math
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...
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...
12:49
Algebra II: binomial Expansion and Combinatorics
65 (done another way) - 66, combinatorics and binomial expansions...
published:24 Dec 2008
Algebra II: binomial Expansion and Combinatorics
Algebra II: binomial Expansion and Combinatorics
published:24 Dec 2008
views:44118
65 (done another way) - 66, combinatorics and binomial expansions
47:42
Extremal and Probabilistic Combinatorics Aula 1 Parte 1
Extremal and Probabilistic Combinatorics
In this course we will introduce the student to ...
published:03 Feb 2015
Extremal and Probabilistic Combinatorics Aula 1 Parte 1
Extremal and Probabilistic Combinatorics Aula 1 Parte 1
published:03 Feb 2015
views:3
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
10:23
Counting and Combinatorics in Discrete Math Part 1
This is part 1 of learning basic counting and combinations in discrete mathematics. I will...
published:01 Dec 2014
Counting and Combinatorics in Discrete Math Part 1
Counting and Combinatorics in Discrete Math Part 1
published:01 Dec 2014
views:7
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.
http://www.manhattanreview.com/gmat-online/. This video covers the permutation and combinatorics problems in the GMAT Math Problem Solving section. Manhattan...
20:31
How To Do Combinatorics In Poker
http://nitreg.com How do you figure out combinatorics in poker? Watch this video and find ...
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...
18:10
Unizor - Combinatorics - Permutations
Combinatorics is a much newer part of mathematics than such classical subjects as Geometry...
published:12 May 2014
Unizor - Combinatorics - Permutations
Unizor - Combinatorics - Permutations
published:12 May 2014
views:1
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!
89:36
Alexander Postnikov (MIT) The Combinatorics of the Grassmanian I
Alexander Postnikov (MIT)
The Combinatorics of the Grassmanian I...
published:22 Jan 2015
Alexander Postnikov (MIT) The Combinatorics of the Grassmanian I
Alexander Postnikov (MIT) The Combinatorics of the Grassmanian I
published:22 Jan 2015
views:40
Alexander Postnikov (MIT)
The Combinatorics of the Grassmanian I
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