So I'm trying to write code for a modified version of the rod cutting problem. The link gives a good intuition of the problem. However, I want to modify the code to not only actually return the solution, i.e. what cuts give the optimal solution, but also limit the number of cuts to a maximum of k.
For proof of concept, I'm trying to create an algorithm to achieve this. The following is what I have so far, I think it successfully returns the actual solution, however, I can't figure out how to limit the maximum to k.
let r[0..n] be a new array
r[0] = 0
for j = 1 to n
q = -1
for i = 1 to j
for k = 0 to n-1
q = Math.max(q[n][k], p[i] + q[n-i-1][k-1]);
r[j] = q
return r[n]
Please do not provide with actual code in your answers, I want to implement that myself, I just need help tweaking my algorithm to give the correct solution.
Update 1: I am already able to find optimal solution for a maximum of k cuts by adding a second dimension to my array. This is shown in the above code.
As you say, you already have the optimal solution, this answer includes only how to retrace the exact solution (cuts made at each step).
Store the candidate cut for length = n and maximum cuts = k
For this, you simply need a 2-d array (say, visit[n][k]) to store the cut made that gets the maximum solution to q[n][k]. In terms of pseudo code and recurrence relations, it will look like the following.
for each value of i:
q[n][k] = q[n][k-1]
visit[n][k] = -1
if q[n][k] < p[i] + q[n-i-1][k-1]:
q[n][k] = p[i] + q[n-i-1][k-1]
visit[n][k] = i
Explanation
It is possible that we don't have a cut that maximizes the solution. In this case, we initialize visit[n][k] = -1.
Every time, we have a candidate to cut the rod of length n at length=i+1, ie. we could get a better price by a cut, we will store the respective cut in another 2-d array.
Reconstruct the solution
Using this 2-d array (visit[n][k]), to back trace the exact cuts, you can use the following pseudo code (I am deliberately avoiding code since you mentioned you don't need it).
cuts = []
while k > 0:
i = visit[n][k]
if i != -1
// If there is a cut
cuts.push(i + 1)
n = n - i - 1
k = k - 1
Explanation
We iterate from k to 0.
Every time, when visit[n][k] is not -1, ie. it is optimal to cut somewhere, we reassign n after making the cut, ie. n = n - i - 1 and store the resultant cut in the array cuts.
Finally, cuts will contain the exact cuts that led to the optimal solution.
Please note that the pseudo code present in your question is slightly incorrect in terms of variables used in the recurrence relation. q is used both to store the DP 2-d array as well as an integer -1. j is not used in the bottom-up DP at all and is replaced with constant n. q[j][k] is uninitialized. However, the general idea is correct.
Related
I have came across a subset sum problem recently. I was able to solve it for smaller arrays using Java earlier, but in this case I have really no idea what should I do. Brute force and recurrence is probably not an option, as I came across out of memory problem.
So, let's say we have an array of {2500, 3200, 3300}. We are looking for the sum closest to the desired number K = 135000. The main difference is that we can use numbers from the array multiple times.
Ok, if we can use them multiple times, then we can change it to more "traditional" way - just divide K by each of these numbers - that is 54, 42 and 40 - and create a new array, which has those numbers the number of times received from dividing. It would be {2500, 2500, 2500, ... , ... 3300, 3300} and the new array would have the length of 136. Now this is much more than 3.
So - how to solve the closest subset sum problem, where we can pick more than 2 numbers from the array of 136 elements or more using Java?
The thing I want to get is not only the closest sum, but also a list of elements which gave that sum.
I heard and was reading about dynamic programing, approximation algorithms and genetic algorithms, but unfortunately I have no idea about those. I did genetic algorithm for a different case some time ago, but I am not sure how to use it in this case.
Any ideas? I will be really glad for help.
I am not going to solve it for you. but I'll give you the key ideas in pseudocode (aka Python).
We start with a state that represents the following statement: "I don't know how to arrive to any numbers. The best thing I can generate is 0. I have not yet processed the fact that I could get to 0."
In data:
can_generate = set()
todo = [0]
best = 0
K = 135000
What we will do is, while anything is in todo, take off a value, see if it is new to us. If it is, we might update best, and possibly add new values to todo. Like this:
while len(todo):
value = todo.pop()
if value not in can_generate:
can_generate.add(value)
if abs(K-value) < abs(K-best):
best = value
if value < K:
for term in [2500, 3200, 3300]:
todo.append(value + term)
Now that we know the values in can_generate, we search backwards to find how to get there.
answer = []
while 0 < best:
for term in [3300, 3200, 2500]:
if best - term in can_generate:
answer.append(term)
best -= term
break
I saw this method in a book, to do binary search, but I can't understand how it is working no matter how I try. Can someone explain to me exactly how it is working?
the book's explanation did not help :
The idea is to make jumps and slow the speed when we get closer to the
target element.
The variables k and b contain the position in the array and the jump
length. If the array contains the element x , the position of x will
be in the variable k after the search. The time complexity of the
algorithm is O (log n ), because the code in the while loop is
performed at most twice for each jump length.
what I don't get is that how is k iterating in the array? How can we make sure that it will not jump over the target's index? I tried tracing some runs of this program with sample values but couldn't figure out the pattern that k is following to find whether target x exists in the array or not.
int k = 1;
for (int b = n/2; b >= 1; b /= 2) {
while (k+b <= n && t[k+b] <= x) k += b;
}
if (t[k] == x) {} // x was found at index k
note: I do understand clearly the "common binary search algorithm" (the one that uses start, middle, and end indices )
b are the length of the jumps of your current position. As you can see, b starts as n/2 and is divided by 2 at each step up until it reaches 1.
Now, For each b, remember that b is divided by 2 at each step in the for loop, we run a while loop where we add b to to our current position, which is k. We add b to k checking for 2 conditions: k+b is less than n (to make sure we don't go out of bounds), and t[k+b] is less than x, which we are searching.
This effectively means that for each b, we add b to k up until where it would go over the value we are seeking. At this point, the while loop breaks and we divide b to approach slower to the target hoping we don't go over it.
The final b is just one, to make sure we don't miss x if it is just the next element after the position of k.
Look at it this way, a car is racing towards a goal. At first the car is going maximum speed, as it nears the target, it gradually decelerates up until it reaches the target.
The difference with traditional binary search, which makes it a little counter intuitive, is that in traditional binary search, we go over the target and then come back and go over again and in each iteration we decrease the steps that we take back and forth. In this algorithm, we only go forwards (never over the target), but we continuously decrease the length of the steps by dividing b.
I am currently working on this coding problem for class.
Given a sorted array of n distinct values as well as a target value T, determine in O(n) time whether or not there exist two distinct values in the array that sum to T. (For example, if the array contained 3, 5, 6, 7, and 9 and T = 14, then the method you are to write should return true, since 5+9 = 14. It should return false if for the same array of values T = 17.)
So, initially, I just wrote the problem with a nested linear search approach which obviously results in a O(n^2) runtime to establish a baseline to simplify from, however, I have only been able to, so far, simplify it to O(n log(n)). I did this by creating a new array made up of the differences of the Target - array[i] and then comparing the new array to the original array using a binary search nested within a loop that linearly goes up the new array.
I am not asking for an answer but rather a hint at where to look to simplify my code. I feel like the fact that the array is sorted is important in getting it down to O(n) but not sure how to go about doing it.
Thanks for your time!
Imagine you have two pointers (s, e) wich set on start and end of you array.
If you will move them in opposite direction (with specific algorithm) and look at the summ of elements you will see that moving one pointer increase summ and moving other decrease.
Onli thing you need is find balance.
If it doesnt help. Ask for next tip.
Some tips/steps:
1 - Start the iteration by the array[i], which is the nearest lower value from T
2 - Move another pointer to the array[0]
3 - Sum both values and compare with T
4 - If bigger or if lower, do appropriate moving in the pointers and repeat the step 3
A Hint:
Something like Binary Search, start with middle (compare with middle)
we have startindex = 0, endindex = N-1
while(some condition){
middleindex = endindex - startindex / 2, middle = array[middleindex]
if T - array[middleindex] > middle, startindex = middleindex
if T - array[middleindex] < middle, endindex = middleindex
}
It will do the task in O(log(n)) :D
Question: Given a sorted array A find all possible difference of elements from A.
My solution:
for (int i=0; i<n-1; ++i) {
for (int j=i+1; j<n; ++j) {
System.out.println(Math.abs(ai-aj));
}
}
Sure, it's O(n^2), but I don't over count things at all. I looked online and I found this: http://www.careercup.com/question?id=9111881. It says you can't do better, but at an interview I was told you can do O(n). Which is right?
A first thought is that you aren't using the fact that the array is sorted. Let's assume it's in increasing order (decreasing can be handled analogously).
We can also use the fact that the differences telescope (i>j):
a_i - a_j = (a_i - a_(i-1)) + (a_(i-1) - a_(i-2)) + ... + (a_(j+1) - a_j)
Now build a new sequence, call it s, that has the simple difference, meaning (a_i - a_(i-1)). This takes only one pass (O(n)) to do, and you may as well skip over repeats, meaning skip a_i if a_i = a_(i+1).
All possible differences a_i-a_j with i>j are of the form s_i + s_(i+1) + ... + s_(j+1). So maybe if you count that as having found them, then you did it in O(n) time. To print them, however, may take as many as n(n-1)/2 calls, and that's definitely O(n^2).
For example for an array with the elements {21, 22, ..., 2n} there are nā
(n-1)/2 possible differences, and no two of them are equal. So there are O(n2) differences.
Since you have to enumerate all of them, you also need at least O(n2) time.
sorted or unsorted doesn't matter, if you have to calculate each difference there is no way to do it in less then n^2,
the question was asked wrong, or you just do O(n) and then print 42 the other N times :D
You can get another counter-example by assuming the array contents are random integers before sorting. Then the chance that two differences, Ai - Aj vs Ak - Al, or even Ai - Aj vs Aj - Ak, are the same is too small for there to be only O(n) distinct differences Ai - Aj.
Given that, the question to your interviewer is to explain the special circumstances that allow an O(n) solution. One possibility is that the array values are all numbers in the range 0..n, because in this case the maximum absolute difference is only n.
I can do this in O(n lg n) but not O(n). Represent the array contents by an array of size n+1 with element i set to 1 where there is a value i in the array. Then use FFT to convolve the array with itself - there is a difference Ai - Aj = k where the kth element of the convolution is non-zero.
If the interviewer is fond of theoretical games, perhaps he was thinking of using a table of inputs and results? Any problem with a limit on the size of the input, and that has a known solution, can be solved by table lookup. Given that you have first created and stored that table, which might be large.
So if the array size is limited, the problem can be solved by table lookup, which (given some assumptions) can even be done in constant time. Granted, even for a maximum array size of two (assuming 32-bit integers) the table will not fit in a normal computer's memory, or on the disks. For larger max sizes of the array, you're into "won't fit in the known universe" size. But, theoretically, it can be done.
(But in reality, I think that Jens Gustedt's comment is more likely.)
Yes you can surely do that its a little tricky method.
to find differances in O(n) you will need to use BitSet(C++) or any similar Data Structure in respective language.
Initialize two bitset say A and B
You can do as follows:
For each iteration through array:
1--store consecutive differance in BitSet A
2--LeftShift B
3--store consecutive differance in BitSet B
4--take A=A or B
for example I have given code-
Here N is Size of array
for (int i=1;i<N;i++){
int diff = arr[i]-arr[i-1];
A[diff]=1;
B<<=diff;
B[diff]=1;
A=A | B;
}
Bits in A which are 1 will be the differances.
First of all the array need to be sorted
lets think a sorted array ar = {1,2,3,4}
so what we were doing at the O(n^2)
for(int i=0; i<n; i++)
for(int j=i+1; j<n; j++) sum+=abs(ar[i]-ar[j]);
if we do the operations here elaborately then it will look like below
when i = 0 | sum = sum + {(2-1)+(3-1)+(4-1)}
when i = 1 | sum = sum + {(3-2)+(4-2)}
when i = 2 | sum = sum + {(4-3)}
if we write them all
sum = ( -1-1-1) + (2+ -2-2) + (3+3 -3) + (4+4+4 )
we can see that
the number at index 0 is added to the sum for 0 time and substracted from the sum for 3 time.
the number at index 1 is added to the sum for 1 time and substracted from the sum for 2 time.
the number at index 2 is added to the sum for 2 time and substracted from the sum for 1 time.
the number at index 3 is added to the sum for 3 time and substracted from the sum for 0 time.
so for we can say that,
the number at index i will be added to the sum for i time
and will be substracted from the sum for (n-i)-1 time
Then the generalized expression for
each element will be
sum = sum + (i*a[i]) ā ((n-i)-1)*a[i];
I have an algorithm, and I want to figure it what it does. I'm sure some of you can just look at this and tell me what it does, but I've been looking at it for half an hour and I'm still not sure. It just gets messy when I try to play with it. What are your techniques for breaking down an algoritm like this? How do I analyze stuff like this and know whats going on?
My guess is its sorting the numbers from smallest to biggest, but I'm not too sure.
1. mystery(a1 , a2 , . . . an : array of real numbers)
2. k = 1
3. bk = a1
4. for i = 2 to n
5. c = 0
6. for j = 1 to i ā 1
7. c = aj + c
8. if (ai ā„ c)
9. k = k + 1
10. bk = ai
11. return b1 , b2 , . . . , bk
Here's an equivalent I tried to write in Java, but I'm not sure if I translated properly:
public int[] foo(int[] a) {
int k=1;
int nSize=10;
int[] b=new int[nSize];
b[k]=a[1];
for (int i=2;i<a.length;){
int c=0;
for (int j=1;j<i-1;)
c=a[j]+c;
if (a[i]>=c){
k=k+1;
b[k]=a[i];
Google never ceases to amaze, due on the 29th I take it? ;)
A Java translation is a good idea, once operational you'll be able to step through it to see exactly what the algorithm does if you're having problems visualizing it.
A few pointers: the psuedo code has arrays indexed 1 through n, Java's arrays are indexed 0 through length - 1. Your loops need to be modified to suit this. Also you've left the increments off your loops - i++, j++.
Making b magic constant sized isn't a good idea either - looking at the pseudo code we can see it's written to at most n - 1 times, so that would be a good starting point for its size. You can resize it to fit at the end.
Final tip, the algorithm's O(n2) timed. This is easy to determine - outer for loop runs n times, inner for loop n / 2 times, for total running time of (n * (n / 2)). The n * n dominates, which is what Big O is concerned with, making this an O(n2) algorithm.
The easiest way is to take a sample but small set of numbers and work it on paper. In your case let's take number a[] = {3,6,1,19,2}. Now we need to step through your algorithm:
Initialization:
i = 2
b[1] = 3
After Iteration 1:
i = 3
b[2] = 6
After Iteration 2:
i = 4
b[2] = 6
After Iteration 3:
i = 5
b[3] = 19
After Iteration 4:
i = 6
b[3] = 19
Result b[] = {3,6,19}
You probably can guess what it is doing.
Your code is pretty close to the pseudo code, but these are a few errors:
Your for loops are missing the increment rules: i++, j++
Java arrays are 0 based, not 1 based, so you should initialize k=0, a[0], i=1, e.t.c.
Also, this isn't sorting, more of a filtering - you get some of the elements, but in the same order.
How do I analyze stuff like this and know whats going on?
The basic technique for something like this is to hand execute it with a pencil and paper.
A more advanced technique is to decompose the code into parts, figure out what the parts do and then mentally reassemble it. (The trick is picking the boundaries for decomposing. That takes practice.)
Once you get better at it, you will start to be able to "read" the pseudo-code ... though this example is probably a bit too gnarly for most coders to handle like that.
When converting to java, be careful with array indexes, as this pseudocode seems to imply a 1-based index.
From static analysis:
a is the input and doesn't change
b is the output
k appears to be a pointer to an element of b, that will only increment in certain circumstances (so we can think of k = k+1 as meaning "the next time we write to b, we're going to write to the next element")
what does the loop in lines 6-7 accomplish? So what's the value of c?
using the previous answer then, when is line 8 true?
since lines 9-10 are only executed when line 8 is true, what does that say about the elements in the output?
Then you can start to sanity check your answer:
will all the elements of the output be set?
try running through the psuedocode with an input like [1,2,3,4] -- what would you expect the output to be?
def funct(*a)
sum = 0
a.select {|el| (el >= sum).tap { sum += el }}
end
Srsly? Who invents those homework questions?
By the way: since this is doing both a scan and a filter at the same time, and the filter depends on the scan, which language has the necessary features to express this succinctly in such a way that the sequence is only traversed once?