Project Euler 215 - Solving with Sparse Matrices and Vector Multiplication
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I wrote a program to solve Project Euler Problem #215 (see below for description) using memoization, and when I got access to the PE forums, I saw everyone else wrote programs that also used dynamic programming/memoization techniques.
However, from this stackoverflow answer, Dietrich Epp solved it in Haskell using a sparse matrix of NxN dimensions (an adjacency matrix) and then multiplied the matrix by a 1xN vector to arrive at the solution.
Quote from the poster Dietrich Epp:
You compute each possible way to tile a row. Let's say there are N ways to tile a row. Make an NxN matrix. Element i,j is 1 if row i can appear next to row j, 0 otherwise. Start with a vector containing N 1s. Multiply the matrix by the vector a number of times equal to the height of the wall minus 1, then sum the resulting vector.
How come the matrix x vector solution is viable for this problem, as in what property is it exploiting to solve the question?
Thanks in advance!
Problem Description
Consider the problem of building a wall out of 2x1 and 3x1 bricks (horizontal and vertical dimensions) such that, for extra strength, the gaps between horizontally-adjacent bricks never line up in consecutive layers, i.e. never form a "running crack".
For example, the following 9x3 wall is not acceptable due to the running crack shown in red:
There are eight ways of forming a crack-free 93 wall, written W(9,3) = 8.
Calculate W(32,10).
linear-algebra matrices project-euler
edited Dec 22 '18 at 20:19
Glorfindel
3,41381930
asked Oct 20 '11 at 20:01
wklwkl
1136
$endgroup$
add a comment |
$begingroup$
I wrote a program to solve Project Euler Problem #215 (see below for description) using memoization, and when I got access to the PE forums, I saw everyone else wrote programs that also used dynamic programming/memoization techniques.
However, from this stackoverflow answer, Dietrich Epp solved it in Haskell using a sparse matrix of NxN dimensions (an adjacency matrix) and then multiplied the matrix by a 1xN vector to arrive at the solution.
Quote from the poster Dietrich Epp:
You compute each possible way to tile a row. Let's say there are N ways to tile a row. Make an NxN matrix. Element i,j is 1 if row i can appear next to row j, 0 otherwise. Start with a vector containing N 1s. Multiply the matrix by the vector a number of times equal to the height of the wall minus 1, then sum the resulting vector.
How come the matrix x vector solution is viable for this problem, as in what property is it exploiting to solve the question?
Thanks in advance!
Problem Description
Consider the problem of building a wall out of 2x1 and 3x1 bricks (horizontal and vertical dimensions) such that, for extra strength, the gaps between horizontally-adjacent bricks never line up in consecutive layers, i.e. never form a "running crack".
For example, the following 9x3 wall is not acceptable due to the running crack shown in red:
There are eight ways of forming a crack-free 93 wall, written W(9,3) = 8.
Calculate W(32,10).
linear-algebra matrices project-euler
edited Dec 22 '18 at 20:19
Glorfindel
3,41381930
asked Oct 20 '11 at 20:01
wklwkl
1136
$endgroup$
$begingroup$
See my answer at stackoverflow.com: stackoverflow.com/questions/7788462/project-euler-215-in-java/…
$endgroup$
– Thomas Andrews
Oct 21 '11 at 3:14
add a comment |
$begingroup$
I wrote a program to solve Project Euler Problem #215 (see below for description) using memoization, and when I got access to the PE forums, I saw everyone else wrote programs that also used dynamic programming/memoization techniques.
However, from this stackoverflow answer, Dietrich Epp solved it in Haskell using a sparse matrix of NxN dimensions (an adjacency matrix) and then multiplied the matrix by a 1xN vector to arrive at the solution.
Quote from the poster Dietrich Epp:
You compute each possible way to tile a row. Let's say there are N ways to tile a row. Make an NxN matrix. Element i,j is 1 if row i can appear next to row j, 0 otherwise. Start with a vector containing N 1s. Multiply the matrix by the vector a number of times equal to the height of the wall minus 1, then sum the resulting vector.
How come the matrix x vector solution is viable for this problem, as in what property is it exploiting to solve the question?
Thanks in advance!
Problem Description
Consider the problem of building a wall out of 2x1 and 3x1 bricks (horizontal and vertical dimensions) such that, for extra strength, the gaps between horizontally-adjacent bricks never line up in consecutive layers, i.e. never form a "running crack".
For example, the following 9x3 wall is not acceptable due to the running crack shown in red:
There are eight ways of forming a crack-free 93 wall, written W(9,3) = 8.
Calculate W(32,10).
linear-algebra matrices project-euler
edited Dec 22 '18 at 20:19
Glorfindel
3,41381930
asked Oct 20 '11 at 20:01
wklwkl
1136
$endgroup$
I wrote a program to solve Project Euler Problem #215 (see below for description) using memoization, and when I got access to the PE forums, I saw everyone else wrote programs that also used dynamic programming/memoization techniques.
However, from this stackoverflow answer, Dietrich Epp solved it in Haskell using a sparse matrix of NxN dimensions (an adjacency matrix) and then multiplied the matrix by a 1xN vector to arrive at the solution.
Quote from the poster Dietrich Epp:
You compute each possible way to tile a row. Let's say there are N ways to tile a row. Make an NxN matrix. Element i,j is 1 if row i can appear next to row j, 0 otherwise. Start with a vector containing N 1s. Multiply the matrix by the vector a number of times equal to the height of the wall minus 1, then sum the resulting vector.
How come the matrix x vector solution is viable for this problem, as in what property is it exploiting to solve the question?
Thanks in advance!
Problem Description
Consider the problem of building a wall out of 2x1 and 3x1 bricks (horizontal and vertical dimensions) such that, for extra strength, the gaps between horizontally-adjacent bricks never line up in consecutive layers, i.e. never form a "running crack".
For example, the following 9x3 wall is not acceptable due to the running crack shown in red:
There are eight ways of forming a crack-free 93 wall, written W(9,3) = 8.
Calculate W(32,10).
linear-algebra matrices project-euler
linear-algebra matrices project-euler
edited Dec 22 '18 at 20:19
Glorfindel
3,41381930
asked Oct 20 '11 at 20:01
wklwkl
1136
edited Dec 22 '18 at 20:19
Glorfindel
3,41381930
asked Oct 20 '11 at 20:01
wklwkl
1136
edited Dec 22 '18 at 20:19
Glorfindel
3,41381930
edited Dec 22 '18 at 20:19
Glorfindel
3,41381930
edited Dec 22 '18 at 20:19
Glorfindel
3,41381930
3,41381930
asked Oct 20 '11 at 20:01
wklwkl
1136
asked Oct 20 '11 at 20:01
wklwkl
1136
asked Oct 20 '11 at 20:01
wklwkl
1136
1136
$begingroup$
See my answer at stackoverflow.com: stackoverflow.com/questions/7788462/project-euler-215-in-java/…
$endgroup$
– Thomas Andrews
Oct 21 '11 at 3:14
add a comment |
$begingroup$
See my answer at stackoverflow.com: stackoverflow.com/questions/7788462/project-euler-215-in-java/…
$endgroup$
– Thomas Andrews
Oct 21 '11 at 3:14
$begingroup$
See my answer at stackoverflow.com: stackoverflow.com/questions/7788462/project-euler-215-in-java/…
$endgroup$
– Thomas Andrews
Oct 21 '11 at 3:14
$begingroup$
See my answer at stackoverflow.com: stackoverflow.com/questions/7788462/project-euler-215-in-java/…
$endgroup$
– Thomas Andrews
Oct 21 '11 at 3:14
add a comment |
1 Answer
1
active
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This is a nice way of keeping track of the linked recurrence relations. The vector initially starts out with all $1$'s indicating there is one way to make a height $1$ wall with each type of layer on the bottom. After one multiplication by the matrix position $j$ in the vector is the number of ways to have a wall of height $2$ with the bottom layer of type $j$. Each successive multiply adds one layer because position $j$ will have the sum of all the other types of layer that can come above it. So after $n-1$ multiplies you have walls of height $n$. Then at the end you sum over all the types of bottom layer.
answered Oct 21 '11 at 0:00
Ross MillikanRoss Millikan
302k24200375
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$begingroup$
Great, thanks for the explanation!
$endgroup$
– wkl
Oct 21 '11 at 0:46
add a comment |
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1 Answer
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1 Answer
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$begingroup$
This is a nice way of keeping track of the linked recurrence relations. The vector initially starts out with all $1$'s indicating there is one way to make a height $1$ wall with each type of layer on the bottom. After one multiplication by the matrix position $j$ in the vector is the number of ways to have a wall of height $2$ with the bottom layer of type $j$. Each successive multiply adds one layer because position $j$ will have the sum of all the other types of layer that can come above it. So after $n-1$ multiplies you have walls of height $n$. Then at the end you sum over all the types of bottom layer.
answered Oct 21 '11 at 0:00
Ross MillikanRoss Millikan
302k24200375
$endgroup$
$begingroup$
Great, thanks for the explanation!
$endgroup$
– wkl
Oct 21 '11 at 0:46
add a comment |
$begingroup$
This is a nice way of keeping track of the linked recurrence relations. The vector initially starts out with all $1$'s indicating there is one way to make a height $1$ wall with each type of layer on the bottom. After one multiplication by the matrix position $j$ in the vector is the number of ways to have a wall of height $2$ with the bottom layer of type $j$. Each successive multiply adds one layer because position $j$ will have the sum of all the other types of layer that can come above it. So after $n-1$ multiplies you have walls of height $n$. Then at the end you sum over all the types of bottom layer.
answered Oct 21 '11 at 0:00
Ross MillikanRoss Millikan
302k24200375
$endgroup$
$begingroup$
Great, thanks for the explanation!
$endgroup$
– wkl
Oct 21 '11 at 0:46
add a comment |
$begingroup$
This is a nice way of keeping track of the linked recurrence relations. The vector initially starts out with all $1$'s indicating there is one way to make a height $1$ wall with each type of layer on the bottom. After one multiplication by the matrix position $j$ in the vector is the number of ways to have a wall of height $2$ with the bottom layer of type $j$. Each successive multiply adds one layer because position $j$ will have the sum of all the other types of layer that can come above it. So after $n-1$ multiplies you have walls of height $n$. Then at the end you sum over all the types of bottom layer.
answered Oct 21 '11 at 0:00
Ross MillikanRoss Millikan
302k24200375
$endgroup$
This is a nice way of keeping track of the linked recurrence relations. The vector initially starts out with all $1$'s indicating there is one way to make a height $1$ wall with each type of layer on the bottom. After one multiplication by the matrix position $j$ in the vector is the number of ways to have a wall of height $2$ with the bottom layer of type $j$. Each successive multiply adds one layer because position $j$ will have the sum of all the other types of layer that can come above it. So after $n-1$ multiplies you have walls of height $n$. Then at the end you sum over all the types of bottom layer.
answered Oct 21 '11 at 0:00
Ross MillikanRoss Millikan
302k24200375
answered Oct 21 '11 at 0:00
Ross MillikanRoss Millikan
302k24200375
answered Oct 21 '11 at 0:00
Ross MillikanRoss Millikan
302k24200375
answered Oct 21 '11 at 0:00
Ross MillikanRoss Millikan
302k24200375
302k24200375
$begingroup$
Great, thanks for the explanation!
$endgroup$
– wkl
Oct 21 '11 at 0:46
add a comment |
$begingroup$
Great, thanks for the explanation!
$endgroup$
– wkl
Oct 21 '11 at 0:46
$begingroup$
Great, thanks for the explanation!
$endgroup$
– wkl
Oct 21 '11 at 0:46
$begingroup$
Great, thanks for the explanation!
$endgroup$
– wkl
Oct 21 '11 at 0:46
add a comment |
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$begingroup$
See my answer at stackoverflow.com: stackoverflow.com/questions/7788462/project-euler-215-in-java/…
$endgroup$
– Thomas Andrews
Oct 21 '11 at 3:14