How to understand the dynamic programming solution in linear partitioning?
Be aware that there's a small mistake in the explanation of the algorithm in the book, look in the errata for the text "(*) Page 297".
About your questions:
- No, the items don't need to be sorted, only contiguous (that is, you can't rearrange them)
- I believe the easiest way to visualize the algorithm is by tracing by hand the
reconstruct_partitionprocedure, using the rightmost table in figure 8.8 as a guide - In the book it states that m[i][j] is "the minimum possible cost over all partitionings of {s1, s2, ... , si}" into j ranges, where the cost of a partition is the larges sum of elements in one of its parts". In other words, it's the "smallest maximum of sums", if you pardon the abuse of terminology. On the other hand, d[i][j] stores the index position which was used to make a partition for a given pair i,j as defined before
- For the meaning of "cost", see the previous answer
Edit:
Here's my implementation of the linear partitioning algorithm. It's based on Skiena's algorithm, but in a pythonic way; and it returns a list of the partitions.
from operator import itemgetter
def linear_partition(seq, k):
if k <= 0:
return []
n = len(seq) - 1
if k > n:
return map(lambda x: [x], seq)
table, solution = linear_partition_table(seq, k)
k, ans = k-2, []
while k >= 0:
ans = [[seq[i] for i in xrange(solution[n-1][k]+1, n+1)]] + ans
n, k = solution[n-1][k], k-1
return [[seq[i] for i in xrange(0, n+1)]] + ans
def linear_partition_table(seq, k):
n = len(seq)
table = [[0] * k for x in xrange(n)]
solution = [[0] * (k-1) for x in xrange(n-1)]
for i in xrange(n):
table[i][0] = seq[i] + (table[i-1][0] if i else 0)
for j in xrange(k):
table[0][j] = seq[0]
for i in xrange(1, n):
for j in xrange(1, k):
table[i][j], solution[i-1][j-1] = min(
((max(table[x][j-1], table[i][0]-table[x][0]), x) for x in xrange(i)),
key=itemgetter(0))
return (table, solution)
I've implemented Óscar López algorithm on PHP. Please feel free to use it whenever you need it.
/**
* Example: linear_partition([9,2,6,3,8,5,8,1,7,3,4], 3) => [[9,2,6,3],[8,5,8],[1,7,3,4]]
* @param array $seq
* @param int $k
* @return array
*/
protected function linear_partition(array $seq, $k)
{
if ($k <= 0) {
return array();
}
$n = count($seq) - 1;
if ($k > $n) {
return array_map(function ($x) {
return array($x);
}, $seq);
}
list($table, $solution) = $this->linear_partition_table($seq, $k);
$k = $k - 2;
$ans = array();
while ($k >= 0) {
$ans = array_merge(array(array_slice($seq, $solution[$n - 1][$k] + 1, $n - $solution[$n - 1][$k])), $ans);
$n = $solution[$n - 1][$k];
$k = $k - 1;
}
return array_merge(array(array_slice($seq, 0, $n + 1)), $ans);
}
protected function linear_partition_table($seq, $k)
{
$n = count($seq);
$table = array_fill(0, $n, array_fill(0, $k, 0));
$solution = array_fill(0, $n - 1, array_fill(0, $k - 1, 0));
for ($i = 0; $i < $n; $i++) {
$table[$i][0] = $seq[$i] + ($i ? $table[$i - 1][0] : 0);
}
for ($j = 0; $j < $k; $j++) {
$table[0][$j] = $seq[0];
}
for ($i = 1; $i < $n; $i++) {
for ($j = 1; $j < $k; $j++) {
$current_min = null;
$minx = PHP_INT_MAX;
for ($x = 0; $x < $i; $x++) {
$cost = max($table[$x][$j - 1], $table[$i][0] - $table[$x][0]);
if ($current_min === null || $cost < $current_min) {
$current_min = $cost;
$minx = $x;
}
}
$table[$i][$j] = $current_min;
$solution[$i - 1][$j - 1] = $minx;
}
}
return array($table, $solution);
}
The following is a modified implementation of Skienna's Linear partitioning algorithm in python that does not calculate the last k column values except of the answer itself : M[N][K] (a cell calculation only depends on the previous )
A test against the input {1,2,3,4,5,6,7,8,9} (used in Skienna's example in the book ) yields a slightly different matrix M ( given the above modification) but correctly returns the final result (in this example the minimum-cost partitioning of s into k ranges is 17 , and matrix D is used to print the list of dividers positions that lead to this optimum) .
import math
def partition(s, k):
# compute prefix sums
n = len(s)
p = [0 for _ in range(n)]
m = [[0 for _ in range(k)] for _ in range(n)]
d = [[0 for _ in range(k)] for _ in range(n)]
for i in range(n):
p[i] = p[i-1] + s[i]
# initialize boundary conditions
for i in range(n):
m[i][0] = p[i]
for i in range(k):
m[0][i] = s[0]
# Evaluate main recurrence
for i in range(1, n):
"""
omit calculating the last M's column cells
except for the sought minimum cost M[N][K]
"""
if i != n - 1:
jlen = k - 1
else:
jlen = k
for j in range(1, jlen):
"""
- computes the minimum-cost partitioning of the set {S1,S2,.., Si} into j partitions .
- this part should be investigated more closely .
"""
#
m[i][j] = math.inf
# This loop needs to be traced to understand it better
for x in range(i):
sup = max(m[x][j-1], p[i] - p[x])
if m[i][j] > sup:
m[i][j] = sup
# record which divider position was required to achieve the value s
d[i][j] = x+1
return s, d, n, k
def reconstruct_partition(S, D, N, K):
if K == 0:
for i in range(N):
print(S[i], end="_")
print(" | ", end="")
else:
reconstruct_partition(S, D, D[N-1][K-1], K-1)
for i in range(D[N-1][K-1], N):
print(S[i], end="_")
print(" | ", end="")
# MAIN PROGRAM
S, D, N, K = partition([1, 2, 3, 4, 5, 6, 7, 8, 9], 3)
reconstruct_partition(S, D, N, K)