I am trying to solve the MARTIAN problem on SPOJ
My algorithm is as follows:
Define dp[i][j]=max amount of minerals that can be mined in the rectangle form 0,0 to i,j.
Use the recurrence
dp[i][j] = max(dp[i-1][j] + total amount of yeyenum
in the i-th row up to the j-th column,
dp[i][j-1] + total amount of bloggium
in the j-th column up to the cell i-th row)
However such an approach yields a WA (Wrong Answer). Can someone please provide me with a test case where such and approach will not work?
I am not looking for the correct algorithm just a test case where this approach fails as. I've been unable to find the bug myself.
Try this on your code(modified from the example given):
4 4
0 0 10 60
1 3 10 0
4 2 1 3
1 1 20 0
10 0 0 0
1 1 1 10
0 0 5 3
5 10 10 10
0 0
If you start by looking at [4][4], you'll choose Bloggium, because you can get 23 bloggium by going up, and only 22 Yeyenum from going left. However, you're going to miss a huge amount of Yeyenum.
Using your algorithm, you'll get 23 + 22 + 7 + 14 + 10 = 76.
If you choose the large Yeyenum, you'll get 70 + 14 + 10 + 22 = 116(all Yeyenum, since the bloggium gets blocked).
Related
So for example I have divide my map into something like this:
click on link
the matrix representative would be
0 1 0 1 0
1 1 1 1 0
0 1 1 1 1
0 1 0 0 0
one of the way I could divide it into even-ish would be:
click to see
where total square is 11 and since 11/3 gives us a decimal, I need to have 2 space with 4 square and one space with 3 squares.
but I don't know an algorithm that will be able to divide a small map like that.
there is probably a code that will be able to solve that particular map, but what if it is like :
1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0 0
0 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0 1
Each value is a square in the map and 1 is the square that should be considered. 0 is an empty/null space that is not part of the map and should not be taken into consideration when dividing the map.
So far I try a for loop adding all value and divide by 3 to determine how many squares is needed for each space. Also, if I get a decimal, then one space can have one more square than the other. So in this problem there is 36 squares so if I try to divide it into 3 space, then each space would have 12 squares.
So I am looking to see if there is an algorithm that will be able to solve all types of map.
This is actually NP-hard for k>=2, where you want k=3 or k=4:
theorem 2.2 in On the complexity of partitioning graphs into connected subgraphs - M.E. DYER, A.M. FRIEZE
You can get a decent answer by greedily removing nodes from your graph, and backtracking if you can't merge the remaining nodes.
It would help if you gave a more rigorous definition of 'even-ish' - for example, consider a map with 13 nodes - Would you rather have divisions of size (4,4,5), (3,3,3,4), (4,4,4,1), (5,5,3), or (4,4,3,2)?
I have a greedy solution for the following problem. I want to know how to prove it?
The problem is as follows: Given an n x n 2D matrix of non-negative integers, find three cells such that the sum of those cells and adjacent cells are as much as possible. If two chosen cells have common adjacent cells, the adjacent cells only participate once in the sum and two cells are considered adjacent if they share a common edge.
The naive brute force solutions runs in O(n6). I have written a greedy solution that runs in O(n4). The greedy solution uses this idea that the cell with maximum total sum of itself and its adjacent cells is always a part of the answer. I have tested both solutions on several test cases and the results are identical.
In greedy algorithm, first I choose the cell with maximum total sum of itself and adjacent cells and then iterate through all possible pair of cells.
Now my question is that, why this greedy strategy works? I want the proof. Thanks!
It doesn't work. Sorry,
20 1 40 1 40 1 20
20 2 40 3 40 2 20
20 1 40 1 40 1 20
1 1 20 20 20 1 1
1 1 20 2 20 1 1
1 1 20 20 20 1 1
The 3 has the highest sum of itself and all adjacent cells. However picking the 3 cells of value 2 is actually best.
Edit
Apparently you meant something different from me by "adjacent". So try this example:
1 1 1 1 1 1 1
20 2 40 3 40 2 20
1 1 1 20 1 1 1
1 1 20 2 20 1 1
1 1 1 20 1 1 1
1 1 1 1 1 1 1
i=1
while i <= n do:
j=0
k=i
while k%3 == 0 do:
k = k/3
j++
end
print i, j
i++
end
What is Big-O of given algorithm (How do we show my work)? And what does this algorithm output // ?
My answer and approach
O(nlogn). Because the outer loop runs linear time as O(n) while the inner loop is dependent as O(logn).
But I'm not sure if it's logn.
When n = 10,
ij
00
10
20
31
40
50
61
70
80
92
100 ( 10, 0)
When n = 30
i j
1 0
2 0
3 1
4 0
5 0
6 1
7 0
8 0
9 2
10 0
11 0
12 1
13 0
14 0
15 1
16 0
17 0
18 2
19 0
20 0
21 1
22 0
23 0
24 1
25 0
26 0
27 3
28 0
29 0
30 1
Appreciate that every third number in the series from 1 to n will be divisible by 3. In the worst case, such a number would end up being divided by 3 in the k loop log_3(i) times. So the sequence will behave like O(n) two thirds of the time, and like O(n*log3(n)) one third of the time. We can therefore claim that your code is upper bounded by O(n*log3(n)), although there is a bound which is tighter than this.
The code will print each value i in the series along with the "three depth" of that number. By "three depth" I mean how many times we were able to divide i by 3. Obviously, for i values which are not multiples of 3, the depth is 0.
I think this is a very complicated dynamic programming problem.
Two spies each have a secret number in [1..m]. To exchange numbers they agree to meet at the river and "innocently" take turns throwing stones: from a pile of n=26 identical stones, each spy in turn throws at least one stone in the river.
The only information is in the number of stones each thrown in each turn. What is the largest m can be so they are sure they can complete the exchange?
Develop a recursive formula to count. Here is the start of the table; complete it to n=26. (You should not expect a closed form.)
n 1 2 3 4 5 6 7 8 9 10 11 12
m 1 1 1 2 2 3 4 6 8 12 16 23
Here are some hints from our professor: I suggest changing the problem to making the following table: Let R(n,m) be the range of numbers [1..R(n,m)] that A can indicate to B if they start with n stones, and both know that A has to also receive a number in [1..m] from B.
For example, if A needs no more information, R(n,1) can be computed by considering how many stones A could throw (one to n), then B thows 1 (if any remain) and A gets to decide again. The base cases R(0,1) = R(1,1) = 1, and you can write a recursive rule if you are careful at the boundaries. (You should find the Fibonacci numbers for R(n,1).)
If A needs information, then B has to send it by his or her choices, so things are a little more complicated. Here is the start of the table:
n\ m 1 2 3 4 5
0 1 0 0 0 0
1 1 0 0 0 0
2 2 0 0 0 0
3 3 1 0 0 0
4 5 2 1 0 0
5 8 4 2 1 1
6 13 7 4 3 2
7 21 12 8 6 4
8 34 20 15 11 8
9 55 33 27 19 16
From the R(n,m) table, how would you recover the entries of the earlier table (the table showing m as a function of n)?
I'm trying to solve this problem and I'm new to backtracking algorithms,
The problem is about making a pyramid like this so that a number sitting on two numbers is the sum of them. Every number in the pyramid has to be different and less than 100. Like this:
88
39 49
15 24 25
4 11 13 12
1 3 8 5 7
Any pointers on how to do this using backtracking?
Not necessarily backtracking but the property you are asking for is interestingly very similar to the Pascal Triangle property.
The Pascal Triangle (http://en.wikipedia.org/wiki/Pascal's_triangle), which is used for efficient computation of binomial coefficient among other things, is a pyramid where a number is equal to the sum of the two numbers above it with the top being 1.
As you can see you are asking the opposite property where a number is the sum of the numbers below it.
1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
1 5 10 10 5 1
1 6 15 20 15 6 1
1 7 21 35 35 21 7 1
1 8 28 56 70 56 28 8 1
For instance in the Pascal Triangle above, if you wanted the top of your pyramid to be 56, your pyramid will be a reconstruction bottom up of the Pascal Triangle starting from 56 and that will give something like:
56
21 35
6 15 20
1 5 10 10
Again that's not a backtracking solution and this might not give you a good enough solution for every single N though I thought this was an interesting approximation that was worth noting.