I have 3 towers [A, B, C] which I will label in numbers as [1,2,3].
I want to find the position a disk will be in if it moves n times in the reverse direction. So if it starts at B or 2 position and moves back 7 times, it will be in A or 1 position.
Is there a general formula I can use to compute this for any given starting position and any number of moves n? To move forward, I just use
(start_pos + n) % 3.
However, I am not sure about the reverse direction.
The result of a modulus operation between a negative and a positive is a positive. I.e., you can use the same general formula, just subtract the number of steps instead of adding them:
(start_pos - n) % 3
Or, alternatively, define the number of steps moved backwards as a negative number of steps.
I have a string of length N with 2 bits. I am trying to find a function to order these strings. For example:
F(110) = 1
F(101) = 2
F(011) = 3
The strategy I adopted was labeling the bits by their position, so that for the first case K=1 and L=2 and hence
F(1,2) = 1
F(1,3) = 2
F(2,3) = 3
Does anyone have an idea of what this function might be?
If you are dealing with actual strings, sort them alphabetically ascending. If you are dealing with integers, there are some workarounds:
Convert the integer into bit-strings and sort alphabetically ascending.
or
Reverse the bits in the integer (011 becomes 110) and sort numerically ascending.
However, these workarounds might be slow. The function F described by you turns out to be pretty simple (assuming you are given the positions of the 1-bits) and is therefore a good solution.
To come up with an implementation of F we first look at the sequence of all bit-strings with exactly two 1-bits. Here we don't care about the length of the bit-string. We simply increment the bit-strings from left to right (opposed to the usual Arabic interpretation of numbers where you increment from right to left).
Next to the actual bit-string I replaced all 0 by ., the left 1 by l, and the right 1 by r. This makes it easier to see the pattern.
1: 11 lr
2: 101 l.r
3: 011 .lr
4: 1001 l..r
5: 0101 .l.r
6: 0011 ..lr
7: 10001 l...r
8: 01001 .l..r
9: 00101 ..l.r
10: 00011 ...lr
11: 100001 l....r
… … …
The function F is supposed to count the steps needed to increment to a given bit-string.
In the following, L is the index of the left 1-bit and R is the index of the right 1-bit. As in your question, we use 1-based indices. That is, the leftmost character in a string has index 1
For the right 1-bit to move one position to the right, the left 1-bit has to "catch up". If the left 1-bit starts at L=1 then catching up takes R-1 steps (when counting the start step L=1 too). For the right 1-bit to reach a high position, the left 1-bit has to catch up multiple times, as it is returned to the start each time the right 1-bit moves one to the right. Each time, catching up takes a little bit longer as the right 1-bit is further away from the start. The first time it takes 1 step, then 2, then 3, and so on. Thus, For the right 1-bit to reach position R it takes 1+2+3+…+(R-1) steps = (R-1)·(R-2)/2 steps. After that, we only have to move the left 1-bit to its position, which takes L steps. Therefore the function is
F(L,R) := (R-1)·(R-2) / 2 + L
Note that this function only is easy to implement if you know L and R. If you have an integer and would need to determine L and R first, it might be easier and faster to reverse the integer instead and sort numerically ascending. Determining L and R might be slower than reversing the bits in the integer.
Describe and analyze an algorithm that finds the maximum-area rectangular
pattern that appears more than once in a given bitmap. Specifically, given
a two-dimensional array M[1 .. n, 1 .. n] of bits as input, your algorithm
should output the area of the largest repeated rectangular pattern in M.
For example, given the bitmap shown on the left in the figure below, your
algorithm should return the integer 195, which is the area of the 15 x 13
doggo. (Although it doesn’t happen in this example, the two copies of the
repeated pattern might overlap.)
Image: enter image description here
Using term rectangle instead of sub-matrix.
Brute Force Approach O(n^8)
Take two distinct positions A and B in matrix. Then consider all rectangles in the matrix with A as top left corner. Similarly consider all rectangles with B as top left corner. Suppose we have a rectangle with top-left corner A of length l and breadth b, then take corresponding rectangle with top left corner B, length l, breath b (Assuming both such rectangles exist). If every bit of both rectangles match, then we have a pattern of area of lb repeating and if lb is greater than previously seen maximum area, update the maximum area.
Repeat this procedure for all pairs of A and B.
Time Complexity : Without making analysis complex, let us assume that for every point P in matrix,for all rectangles with P as top left corner, possible maximum length of rectangle is n (But this is not true, for example consider point in the last row and last column of matrix). Let us make similar assumption for maximum possible breath of rectangle.
Then from product rule in counting, there are n^2 possible rectangles with point P as their top left corner.
This estimate is not so bad because according to this, there are total n^4 rectangles possible, as there are n^2 points in the rectangle and we have n^2 rectangles per point.
Actual answer is ( (n+1) Choose 2 ) * ( (n+1) Choose 2 ), which is in the order of n^4
So in total, for each pair, we'd compare n^2 rectangles. And similar to above, let us estimate that there are n^2 points in each rectangle for easy calculations. So for a pair of points A and B, we'll have O(n^4) comparisons because for each rectangle, we need to check all of their corresponding points.
And there are ( n^2 Choose 2 ) pairs of points in total, which is of order n^4. So we'd have overall time complexity of O(n^8).
Avoiding Repeated Calculations O(n^6)
We see that we are repeatedly checking same area again and again. For example consider two rectangles with top-left corners at A and B respectively, with length l and breadth b. Again when we check another corresponding rectangles with top-left corners at A and B with length (l+1) and breadth b, we are again checking rectangle of length l and breadth b.
So we use memoization to avoid repeated calculations.
Assume length of a rectangle is measured horizontally and breadth vertically measured.
Consider rectangles of length l+1 and breadth b with top-left corners at A and B. Now we need to compare if all values of corresponding points in rectangles match. A_r, B_r be points to right of A and B respectively. Then if rectangles have same values for all corresponding points, rectangles of length l, breadth b with top left corners A and B must repeat. Similarly rectangles of length l and breadth b with top-left corners at A_r and B_r must match.
Consider below figure :
Time Complexity By above procedure, it'd take O(1) time to compare two rectangles. So compared to above case, time complexity is reduced by a factor of n^2 (which was time required to compare rectangles in earlier case). So O(n^6) time in this case.
Let us represent (P, m, n) as rectangle with top-left corner P, length m and breadth m.
Removing unnecessary calculations O(n^5)
In the above approach, even if we know that if rectangles (A, l, b) and (B, l, b) do not match we again compare the rectangles (A, l+1, b) and (B, l+1, B).
So suppose now we have rectangles with top-left corners A and B each of length l, then what is maximum possible breadth of the rectangles?
If all the corresponding elements in top row of each rectangle do not match, then answer is 0. But if all corresponding rows match, then the answer is 1 + (max.breadth of rectangles of length l but with top-left corners below A and B).
And similar to above approach this calculation would take O(1) time, as we need breadth that of (A, B, l-1) and that of below rectangles with length l.
Refer to this answer Largest Square Block for more clarity.
Time Complexity For each pair of points in the matrix, we have to store max. breadth for length 1, 2, ...n. And there are order of n^4 such pairs. And we can check obtain each value in O(1) time. So overall time complexity is O(n^5). And finding max. area for each length of rectangle for each pair is obvious here.
Further Optimization O(n^4)
Imagine you have two copies of bitmaps. One is glued to the ground and other can move on top the glued one. Let us call fixed one as base and other one as moving bitmap.
Now top-left corner of moving bitmap can be on one of (n^2 - 1) points of base, except the case where moving bitmap sits on top of base. Now in each case, there are some points left-out i.e for some points on base bitmap, there will not be a point of moving one on top of it and vice-versa. Assume top-left corner of moving bitmap needs to have an element of base bitmap below it.
Now take one instance of these (n^2 - 1) configurations. And for all points on moving bitmap which have a point of base bitmap below them, let us construct a new matrix such that it contains "Y" if top and bottom element of both the bitmaps are same, else it'd contain "N". And remember, the size of matrix is same as no of elements on moving bitmap which have an element of base bitmap below them.
Then, maximum area of repeated pattern for that portions of base and moving bitmaps would be maximum solid block area containing all Y's, which can be done in O(n^2) time.
Our required answer is maximum of answers in all these (n^2 - 1) configurations.
Refer to Largest rectangle containing all Y's for further clarity
Time Complexity For each configuration, constructing new matrix of "Y" and "N"'s would take O(n^2) time and maximum area calculation also O(n^2) time.
And there are (n^2- 1) such configurations, so overall O(n^4) time.
Further Optimization O(n^3 polylog(n))
Consider in above bitmap :
For length : 1, a rectangle with length : 1 and breadth 3 is
repeating twice. (1st and 2nd columns are same in bitmap). So
maxBreadth(1) is 3.
For length : 2, a rectangle with length : 2 and breadth 2 is
repeating twice. So maxBreadth(2) is 2.
The rectangle is :
0
0
0
0
For length : 3, a rectangle with length : 3 and breadth 1 is
repeating twice. So maxBreadth(3) is 1.
For lengths : 4, no rectangle with length : 4 is repeating in the
bitMap, so maxBreadth(4) is 0.
Consider you have a method named maxBreadth which takes a bitmap matrix and length L as input and returns to you the maximum breadth B for which there is a repeating rectangle of with length L and breadth B in the bitmap.
Using this, can you find the area of largest repeating rectangle in a bitmap?? Iterate over each length. We now know that there is a rectangle repeating in the bitmap with area length * maxBreadth(bitMap, length). Update the corresponding maximum area encountered till now.
So now let's focus on maxBreadth method.
Observations :
If a rectangle of length 3 and breadth 5 is not repeating in given bitmap then a rectangle of length 3 and breadth 6 definitely will not be repeating in the bitmap. Generally if a rectangle of length l and breadth b does not repeat in bitMap, any rectangle of length l and breadth > b does not repeat in bitMap. Same goes for length also.
So based on above observation, you can do binary search to find maxBreadth of given length.
If rectangle of length L breadth B is
repeating, then check for breadth 2B
not repeating, then check for breadth B/2
Ok, now how to check if any rectangle of dimensions (l, b) is repeating in a bitMap? There are n^2 such rectangles, so will you compare each with all the others?
What will you do if you asked if an array of numbers is having a repeated element efficiently? Answer is to sort them and check
So take all the n^2 rectangles and sort them and check if there is any repeating one. And how to compare two 2D rectangles, just divide the rectangle into 4 quadrants and compare them individually. In this way we only need to store sorted indices for breadths 1, 2, 4, 8, ... n. and also for lengths 1, 2, 4, 8, ....n.
Each sorting takes O(n^2 log(n)) time for given (length, breadth)
And for each length we perform this operation log(n) times, so total O((n.logn)^2) time complexity for maxBreadth operation.
Finally we call the method maxBreadth n times, so overall time complexity is O(n^3 log(n)^2) and space complexity is O((n.logn)^2)
Note : This O(n^3 log(n)^2) method not only works for bitMaps but also for matrices containing any number of distinct arbitrary numbers to search for maximum repeating sub-matrix
So, the problem is:
We have an urn with N balls, each numbered from 1 to n.
We keep drawing without putting back, and the question is whats the chance that one of the balls with number k (k can be anything) will be drawn exactly after k-1 draws?
should be 1/n.
Approach 1: You only care about the kth draw, which must be a precise ball. Odds of that are 1/n, the other draws are free choices otherwise.
Approach 2: (grind the raw probability)
The first (k-1) draws each have probability (n-i)/(n-i+1), which when multiplied reduces to (n-k+1)/n
The kth draw probability is 1/(n-k+1)
(n-k+1)/n * 1/(n-k+1) = 1/n
Given a sequence a1a2....a_{m+n} with n +1s and m -1s, if for any 1=< i <=m+n, we have
sum(ai) >=0, i.e.,
a1 >= 0
a1+a2>=0
a1+a2+a3>=0
...
a1+a2+...+a_{m+n}>=0
then the number of sequence that meets the requirement is C(m+n,n) - C(m+n,n-1), where the first item is the total number of sequence, and the second term refers to those sub-sum < 0.
I was wondering whether there is a similar formula for the bi-side sequence number :
a1 >= 0
a1+a2>=0
a1+a2+a3>=0
...
a1+a2+...+a_{m+n}>=0
a_{m+n}>=0
a_{m+n-1}+a_{m+n}>=0
...
a1+a2+...+a_{m+n}>=0
I feel like it can be derived similarly with the single-side subsum problem, but the number C(m+n,n) - 2 * C(m+n,n-1) is definitely incorrect. Any ideas ?
A clue: the first case is a number of paths (with +-1 step) from (0,0) to (n+m, n-m) point, where path never falls below zero line. (Like Catalan numbers for parenthesis pairs, but without balance requirement n=2m)
Desired formula is a number of (+-1) paths which never rise above (n-m) line. It is possible to get recursive formulas. I hope that compact formula exists for it.
If we consider lattice path at nxm grid, where horizontal step for +1 and vertical step for -1, then we need a number of paths restricted by parallelogramm with (n-m) base