I have read this: https://www.topcoder.com/community/competitive-programming/tutorials/binary-search.
I can't understand some parts==>
What we can call the main theorem states that binary search can be
used if and only if for all x in S, p(x) implies p(y) for all y > x.
This property is what we use when we discard the second half of the
search space. It is equivalent to saying that ¬p(x) implies ¬p(y) for
all y < x (the symbol ¬ denotes the logical not operator), which is
what we use when we discard the first half of the search space.
But I think this condition does not hold when we want to find an element(checking for equality only) in an array and this condition only holds when we're trying to find Inequality for example when we're searching for an element greater or equal to our target value.
Example: We are finding 5 in this array.
indexes=0 1 2 3 4 5 6 7 8
1 3 4 4 5 6 7 8 9
we define p(x)=>
if(a[x]==5) return true else return false
step one=>middle index = 8+1/2 = 9/2 = 4 ==> a[4]=5
and p(x) is correct for this and from the main theory, the result is that
p(x+1) ........ p(n) is true but its not.
So what is the problem?
We CAN use that theorem when looking for an exact value, because we
only use it when discarding one half. If we are looking for say 5,
and we find say 6 in the middle, the we can discard the upper half,
because we now know (due to the theorem) that all items in there are > 5
Also notice, that if we have a sorted sequence, and want to find any element
that satisfies an inequality, looking at the end elements is enough.
Related
For the list of items in an array i.e. {23 , 12, 8, 15, 21}; the number of passes I could see is only 3 contrary to (n-1) passes for n elements. I also see that the (n-1) passes for n elements is also the worst case where all the elements are in descending order. So I have got 2 conclusions and I request you to let me know if my understanding is right wrt the conclusions.
Conclusion 1
(n-1) passes for n elements can occur in the following scenarios:
all elements in the array are in descending order which is the worst case
when it is not the best case(no of passes is 1 for a sorted array)
Conclusion 2
(n-1) passes for n elements is more like a theoretical concept and may not hold true in all cases as in this example {23 , 12, 8, 15, 21}. Here the number of passes are (n-2).
In a classic, one-directional bubble sort, no element is moved more than one position to the “left” (to a smaller index) in a pass. Therefore you must make n-1 passes when (and only when) the smallest element is in the largest position. Example:
12 15 21 23 8 // initial array
12 15 21 8 23 // after pass 1
12 15 8 21 23 // after pass 2
12 8 15 21 23 // after pass 3
8 12 15 21 23 // after pass 4
Let's define L(i) as the number of elements to the left of element i that are larger than element i.
The array is sorted when L(i) = 0 for all i.
A bubble sort pass decreases every non-zero L(i) by one.
Therefore the number of passes required is max(L(0), L(1), ..., L(n-1)). In your example:
23 12 8 15 21 // elements
0 1 2 1 1 // L(i) for each element
The max L(i) is L(2): the element at index 2 is 8 and there are two elements left of 8 that are larger than 8.
The bubble sort process for your example is
23 12 8 15 21 // initial array
12 8 15 21 23 // after pass 1
8 12 15 21 23 // after pass 2
which takes max(L(i)) = L(2) = 2 passes.
For a detailed analysis of bubble sort, see The Art of Computer Programming Volume 3: Sorting and Searching section 5.2.2. In the book, what I called the L(i) function is called the “inversion table” of the array.
re: Conclusion 1
all elements in the array are in descending order which is the worst case
Yep, this is when you'll have to do all (n-1) "passes".
when it is not the best case (no of passes is 1 for a sorted array)
No. When you don't have the best-case, you'll have more than 1 passes. So long as it's not fully sorted, you'll need less than (n-1) passes. So it's somewhere in between
re: Conclusion 2
There's nothing theoretical about it at all. You provide an example of a middle-ground case (not fully reversed, but not fully sorted either), and you end up needing a middle-ground number of passes through it. What's theoretical about it?
You’re given a chess board with dimension n x n. There’s a king at the bottom right square of the board marked with s. The king needs to reach the top left square marked with e. The rest of the squares are labeled either with an integer p (marking a point) or with x marking an obstacle. Note that the king can move up, left and up-left (diagonal) only. Find the maximum points the king can collect and the number of such paths the king can take in order to do so.
Input Format
The first line of input consists of an integer t. This is the number of test cases. Each test case contains a number n which denotes the size of board. This is followed by n lines each containing n space separated tokens.
Output Format
For each case, print in a separate line the maximum points that can be collected and the number of paths available in order to ensure maximum, both values separated by a space. If e is unreachable from s, print 0 0.
Sample Input
3
3
e 2 3
2 x 2
1 2 s
3
e 1 2
1 x 1
2 1 s
3
e 1 1
x x x
1 1 s
Sample Output
7 1
4 2
0 0
Constraints
1 <= t <= 100
2 <= n <= 200
1 <= p <= 9
I think this problem could be solved using dynamic-programing. We could use dp[i,j] to calculate the best number of points you can obtain by going from the right bottom corner to the i,j position. We can calculate dp[i,j], for a valid i,j, based on dp[i+1,j], dp[i,j+1] and dp[i+1,j+1] if this are valid positions(not out of the matrix or marked as x) and adding them the points obtained in the i,j cell. You should start computing from the bottom right corner to the left top, row by row and beginning from the last column.
For the number of ways you can add a new matrix ways and use it to store the number of ways.
This is an example code to show the idea:
dp[i,j] = dp[i+1,j+1] + board[i,j]
ways[i,j] = ways[i+1,j+1]
if dp[i,j] < dp[i+1,j] + board[i,j]:
dp[i,j] = dp[i+1,j] + board[i,j]
ways[i,j] = ways[i+1,j]
elif dp[i,j] == dp[i+1,j] + board[i,j]:
ways[i,j] += ways[i+1,j]
# check for i,j+1
This assuming all positions are valid.
The final result is stored in dp[0,0] and ways[0,0].
Brief Overview:
This problem can be solved through recursive method call, starting from nn till it reaches 00 which is the king's destination.
For the detailed explanation and the solution for this problem,check it out here -> https://www.callstacker.com/detail/algorithm-1
I'm trying to create a binary to decimal converter, and have got stuck on the code. I have researched forums for any help, but they all seam to use functions, which can not be used within a private sub. Please can anyone give me help on a solution to this problem?
I would use the positional notation method:
http://en.wikipedia.org/wiki/Positional_notation
http://www.wikihow.com/Convert-from-Binary-to-Decimal
So basically, without giving you the answer, you want to loop through binary place holders, filling up a variable as you go along. You would use an index to move from the least significant placeholder to the most.
For example : 10011011 in binary is 155 decimal.
So every place holder is a power with a base of two. Then you add the value for each one until your finished, like so:
placeholder 1 is: 2 pow 0 equals 1.
placeholder 2 is: 2 pow 1 equals 2.
placeholder 3 is: 2 pow 2 equals 4.
placeholder 4 is: 2 pow 3 equals 8.
placeholder 5 is: 2 pow 4 equals 16.
placeholder 6 is: 2 pow 5 equals 32.
placeholder 7 is: 2 pow 6 equals 64.
placeholder 8 is: 2 pow 7 equals 128.
Now we only add for the placeholders that have 1s.
128+16+8+2+1 = 155
What you will need:
A loop looping through indexes, and incrementing the exponent value as you go along, only adding the value if the index equals 1 in the binary number.
Hope my explanation makes sense. Good luck.
I don't know whether the term "Lazy" Binary Search is valid, but I was going through some old materials and I just wanted to know if anyone can explain the algorithm of a Lazy Binary Search and compare it to a non-lazy Binary Search.
Let's say, we have this array of numbers:
2, 11, 13, 21, 44, 50, 69, 88
How to look for the number 11 using a Lazy Binary Search?
Justin was wrong.
The common binarySearch algorithm first checks whether the target is equal to current middle entry before proceeding to the left or right halves if required. Lazy binarySearch algorithm postpones the equality check until the very end.
algorithm lazyBinarySearch(array, n, target)
left<-0
right<-n-1
while (left<right) do
mid<-(left+right)/2
if(target>array[mid])then
left<-mid+1
else
right<-mid
endif
endwhile
if(target==array[left])then
display "found at position", left
else
display "not found"
endif
In your case, in an array,
2 11 13 21 44 50 69 88
and you want to search for 11
First we do a trace of common binary search,
index 0 1 2 3 4 5 6 7
2 11 13 21 44 50 69 88
left mid right
First while loop:
left <= right, we enter the first while loop. We calculated the mid index by (0+7)/2=3.5=3 by integer division, mid = 3. straight away we check if target 11 is equal to the mid index entry, 11 != 21, then we decide whether to go left or right, we finds out 11 < 21, should go left. left index remains unchanged, right index becomes mid index -1, right = 3 - 1 = 2. Done this step.
Second while loop:
left <= right, 0 <= 2, we enter the seond while loop. Mid index is recalcuated: (0+2)/2=1, mid = 1. At once we do the equality check, target 11 is the same as the index 1 entry, 11 == 11. We found this entry, leaving behind all the left right mid indexes (don't care) and breaks out the while loop, return index 1.
Now we trace this search by lazy binazySearch algorithm, initial array with left/right indexes set up the same as previous.
First while loop:
left < right, we enter the first while loop. Mid index is calculated as the same above = 3. Instead of doing an equality check in common binarySearch we do a comparison with the mid index entry this time. And the comparison only checks if our target 11 is greater than the mid index entry, leaving whether they equal or not to the very end outside the while loop. So we find out 11 < 21, right index is reset to the mid index, right = 3. Done this step.
Second while loop:
left < right, 0 < 3, we enter the second while loop. mid index is recalculated as mid = (0+3)/2 = 1 by integer division. Again we do a comparison with mid index entry 11, we realise it's not greater than mid index entry. We fall into the else part of the while loop and reset the right index to be mid index, right = 1. Done this step.
Third while loop:
left < right, 0 < 1, this time we have to re-enter the while loop again since it still satisfies the while condition. Mid index becomes (0+1)/2=0, mid = 0. After comparing target 11 with mid index entry 2 we found out it's greater than it (11 > 2), left index is reset to mid + 1, left = 0 + 1 = 1. Done this step.
With left index = 1 and right index = 1, left = right, while loop condition is no longer satisfied, so there's no need to re-enter. We fall into the if part down below. Target 11 equals left index entry 11, we found the target and returns left index 1.
As you can see, lazy binarySearch does one more while loop step to finally realise the index is actually 1. And this is how the word "postpones the equality check" means in the definition I mentioned in the very beginning. Clearly the lazy binarySearch algorithm does more things than common binarySearch before reaching the program termination. And the term "lazy" is reflected in the time of when to check the target equals the mid index entry.
However lazy binarySearch is more preferable to use under some other circumstances, but it's not in the context of this case.
(The reasoning part of the algorithm is done by me, anyone wishes to copy please do credit)
source: "Data Structures Outside In With Java" by Sesh Venugopal, Prentice Hall
As far as I am aware "Lazy binary search" is just another name for "Binary search".
I read in a book on non-deterministic mapping there is mapping from Q*∑ to 2Q for M=(Q,∑,trans,q0,F)
where Q is a set of states.
But I am not able to understand how it's 2Q;
if there are 3 states a, b, c, how does it map to 8 states?
I always found that the easiest way to think about these (since the set of states is finite) is as having each of those subsets be an encoding of a base-2 number that ranges from 0 (all bits zero) to 2|Q|-1 (all bits one), where there are as many bits in the number as there are members in the state set, Q. Then, you can just take one of these numbers and map it into a subset by using whether a particular bit in the number is set. Easy!
Here's a worked example where Q = {a,b,c}. In this case, |Q| is 3 (there are three elements) and so 23 is 8. That means we get this if we say that the leading bit is for element a, the next bit is for b, and the trailing bit for c:
0 = 000 = {}
1 = 001 = {c}
2 = 010 = {b}
3 = 011 = {b,c}
4 = 100 = {a}
5 = 101 = {a,c}
6 = 110 = {a,b}
7 = 111 = {a,b,c}
See? That initial three states has been transformed into 8, and we have a natural numbering of them that we could use to create the labels of those states if we chose.
Now, to the interpretations of this within a non-deterministic context. Basically, the non-determinism means that we're uncertain about what state we're in. We represent this by using a pseudo-state that is the set of “real” states that we might be in; if we have total non-determinism then we are in the pseudo-state where all real-states are possible (i.e., {a,b,c}) whereas the pseudo-state where no real-states are possible (i.e., {}) is the converse (and really ought to be impossible to reach in the transition system). In a real system, you're usually not dealing with either of those extremes.
The logic of how you convert the deterministic transition system into a non-deterministic one is rather more complex than I want to go into here. (I had to read a substantial PhD thesis to learn it so it's definitely more than an SO answer's worth!)
2Q means the set of all subsets of Q. For each state q and each letter x from sigma, there is a subset of Q states to which you can go from q with letter x. So yeah, if there are three states abc the set 2Q consists of 8 elements {{}, {a}, {b}, {c}, {a,b}, {a,c}, {b,c}, {a,b,c}}. It doesn't map to 8 states, it maps to one of these 8 sets. HTH