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.
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...
15:01
Combinatorics with Day[9]: Bijections
Combinatorics with Day[9]: Bijections
Combinatorics with Day[9]: Bijections
Combinatorics with Day[9]: Bijections.
26:47
Basic Combinatorics
Basic Combinatorics
Basic Combinatorics
I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
23:03
Unizor - -Combinatorics - Combinations with Repetitions
Unizor - -Combinatorics - Combinations with Repetitions
Unizor - -Combinatorics - Combinations with Repetitions
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 nu
57:22
Combinatorics Recurrence Relations Part 1
Combinatorics Recurrence Relations Part 1
Combinatorics Recurrence Relations Part 1
The video helps in understanding the concept of Recurrene Relations.
http://www.manhattanreview.com/gmat-online/. This video covers the permutation and combinatorics problems in the GMAT Math Problem Solving section. Manhattan...
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...
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.
15:08
Probability using Combinatorics
Probability using Combinatorics
Probability using Combinatorics
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
21:22
Unizor - Combinatorics - Advanced Problems 1.1.
Unizor - Combinatorics - Advanced Problems 1.1.
Unizor - Combinatorics - Advanced Problems 1.1.
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...
Learn more: http://www.khanacademy.org/video?v=8TIben0bJpU A different way to think about the probability of getting 2 heads in 4 flips.
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...
15:01
Combinatorics with Day[9]: Bijections
Combinatorics with Day[9]: Bijections
Combinatorics with Day[9]: Bijections
Combinatorics with Day[9]: Bijections.
26:47
Basic Combinatorics
Basic Combinatorics
Basic Combinatorics
I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
23:03
Unizor - -Combinatorics - Combinations with Repetitions
Unizor - -Combinatorics - Combinations with Repetitions
Unizor - -Combinatorics - Combinations with Repetitions
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 nu
57:22
Combinatorics Recurrence Relations Part 1
Combinatorics Recurrence Relations Part 1
Combinatorics Recurrence Relations Part 1
The video helps in understanding the concept of Recurrene Relations.
http://www.manhattanreview.com/gmat-online/. This video covers the permutation and combinatorics problems in the GMAT Math Problem Solving section. Manhattan...
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...
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.
15:08
Probability using Combinatorics
Probability using Combinatorics
Probability using Combinatorics
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
21:22
Unizor - Combinatorics - Advanced Problems 1.1.
Unizor - Combinatorics - Advanced Problems 1.1.
Unizor - Combinatorics - Advanced Problems 1.1.
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...
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...
8:28
Combinatorics Problems
Combinatorics Problems
Combinatorics Problems
13:29
Enumerative combinatorics
Enumerative combinatorics
Enumerative combinatorics
(C) 2012 David Liao lookatphysics.com CC-BY-SA Permutations and factorials Combinations Binomial theorem Small parameter expansion.
4:15
Intuitive connection between binomial expansion and combinatorics
Intuitive connection between binomial expansion and combinatorics
Intuitive connection between binomial expansion and combinatorics
Description
6:23
GMAT Content: Combinations and Permutations | Kaplan Test Prep
GMAT Content: Combinations and Permutations | Kaplan Test Prep
GMAT Content: Combinations and Permutations | Kaplan Test Prep
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...
29:16
0.4 Analytic Combinatorics 2915)
0.4 Analytic Combinatorics 2915)
0.4 Analytic Combinatorics 2915)
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
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...
5:37
Combinatorics: N choose K, permutations and subsets
Combinatorics: N choose K, permutations and subsets
Combinatorics: N choose K, permutations and subsets
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...
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.
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://www.manhattanreview.com/gmat-online/. This video covers the permutation and combinatorics problems in the GMAT Math Problem Solving section. Manhattan...
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...
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.
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
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 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...
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...
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...
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...
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...
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...
Unizor - -Combinatorics - Combinations with Repetitions
In this lecture we will discuss a different type of combinations - the combinations with r...
published:19 May 2014
Unizor - -Combinatorics - Combinations with Repetitions
Unizor - -Combinatorics - Combinations with Repetitions
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.
published:19 May 2014
views:447
57:22
Combinatorics Recurrence Relations Part 1
The video helps in understanding the concept of Recurrene Relations....
http://www.manhattanreview.com/gmat-online/. This video covers the permutation and combinatorics problems in the GMAT Math Problem Solving section. Manhattan...
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...
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
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.
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
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
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Unizor - Combinatorics - Advanced Problems 1.1.
Problem 1 What is the number of partial permutations of N given objects by K objects under...
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...
The gunman killed by police after the Tunisian attack which killed at least 38 people was not previously known to the authorities, according to the country’s prime minister. Habib Essid said Seifeddine Rezgui came from the town of Gaafour in the Siliana region and had been a student at the University of Kairouan... ....
One of the two New York prison escapees has been shot and the other is being pursued, according to a report from CNN... Here's more from the Buffalo News. ... ....
Article by WN.com Correspondent DallasDarling. The world must come to terms with hard truths instead of pleasant sounding euphemisms and soundbites ... Unlike the U.S ... Coupled with mindless policies, the explosion of individual, tribal, stateless and state-sponsored acts of terrorism will continue to soar ... But even progressive political and military ideas or modern human nature are extensions of the historical past....
A decapitated head has been found posted on the gate at the entrance to a gas factory in south-eastern France which came under attack from terrorists this morning, security officials said. Police sources said the decapitated head was found at the site along with a flag bearing Islamist inscriptions. Local newspaper Le Dauphine said the head was also covered in Arabic writing... He said they attacked the factory “using gas canisters”....
WASHINGTON — A deeply divided Supreme Court on Friday delivered a historic victory for gay rights, ruling 5-4 that the Constitution requires that same-sex couples be allowed to marry no matter where they live. The court's action rewarded years of legal work by advocates of same-sex marriage and marked the culmination of an unprecedented upheaval in public opinion and the nation's jurisprudence ... (Mladen Antonov, AFP/Getty Images)....
(Source. Wilfrid Laurier University). WATERLOO - The Natural Sciences and EngineeringResearch Council (NSERC) awarded 17 Wilfrid Laurier University researchers a total of $1.73 million dollars in funding. Two Laurier students received $175,000 in doctoral scholarships ... A wide range of Laurier research projects received funding, including ... research ... algebraic combinatorics of symmetric functions ($55,000) Ian Hamilton, chemistry ... (noodl....
It's very common to talk about raising student achievement. After all, this is where we see gaps. Doing poorly on an assignment or failing a test denotes "poor achievement" and stands out to teachers, parents and society. We naturally want to fix student achievement. But here's the twist ... She worked and talked through the problem for more than 20 minutes, ultimately solving a high school-level combinatorics concept about permutations....
Conferred with the Padma Shri award this year, Roy's term as the head of the ISI was set to expire in July ... "There is justified and a reasonable apprehension that the present director Dr B ... in combinatorics and optimization from the University of Waterloo, Roy was appointed as the ISI director in August 2010 ... ....
Biologist�Dr. E.O.Wilson spent many countless childhood hours watching ants. His unremitting interest eventually led to his ground-breaking research into sociobiology and biodiversity, becoming renown as the world's leading expert on myrmecology. Dr ... He's received many major mathematical accolades in areas of combinatorics, harmonic analysis, matrix theory, and other areas. Dr ... This motivation was given a name by Dr ... Myths & Realities ... Dr....
representation theory of Lie algebras, algebraic groups and quantum groups, algebraic combinatorics, modular representation theory of finite groups, and invariant theory ... the Journal of AlgebraicCombinatorics and AMS' Representation Theory, being the dissertation advisor for five doctoral students, and winning multiple outstanding teaching awards....
Guzzlers, how did you get on?. Let me first restate the problem. Albert, Bernard and Cheryl became friends with Denise, and they wanted to know when her birthday is. Denise gave them a list of 20 possible dates. Denise then told Albert, Bernard and Cheryl separately the month, the day and the year of her birthday respectively. Albert...Bernard ... Cheryl ... Albert. Now I know when Denise’s birthday is. Bernard ... In the symbology of combinatorics ... ....
The last word on Albert, Bernard, Cheryl and Denise. With workings. Happy birthday, Denise! Photograph. LJSphotography /Alamy. Alex Bellos. Tuesday 26 May 2015 07.30BSTLast modified on Tuesday 26 May 2015 07.34 BST. Guzzlers, how did you get on?. Let me first restate the problem. Albert, Bernard and Cheryl became friends with Denise, and they wanted to know when her birthday is ...Albert ... Bernard ... Cheryl ... In the symbology of combinatorics ... ....
representation theory of Lie algebras, algebraic groups and quantum groups, algebraic combinatorics, modular representation theory of finite groups, and invariant theory ... the Journal of AlgebraicCombinatorics and AMS' Representation Theory, being the dissertation advisor for five doctoral students, and winning multiple outstanding teaching awards....
(Source. College of William and Mary). In recognition of their exemplary achievements in teaching, research and service, 20 William & Mary professors are being honored this year with the university's prestigious Plumeri Award for Faculty Excellence... The award was established in 2009 with a generous gift from Joseph J. Plumeri II '66, D.P.S. '11 ... Vinroot's research interests are in the representation theory of groups and combinatorics....