can anyone pls tell me the time complexity of this func.
this is for generating all valid parenthesis , given the count of pairs
Input: n = 3
Output: ["((()))","(()())","(())()","()(())","()()()"]
My code is working fine, but im not sure about time complexity of it.
pls help
class Solution {
public List generateParenthesis(int n) {
HashMap<String,Boolean> hm = new HashMap<>();
return generate(hm,n);
}
public static List<String> generate(HashMap<String,Boolean> hm, int n ){
if(n == 1){
hm.put("()",true);
List<String>temp = new ArrayList<>();
temp.add("()");
return temp;
}
List<String>temp = generate(hm,n-1);
List<String>ans = new ArrayList<>();
for(String pat : temp){
for(int i = 0; i < pat.length(); i++){
String newPat = pat.substring(0,i)+"()"+pat.substring(i);
if(!hm.containsKey(newPat)){
hm.put(newPat,true);
ans.add(newPat);
}
}
}
return ans;
}
}
You have two for loops, which each run over m and n elements, it can be written as O(m*n), and be contracted to O(n^2) because m can be equal to n.
Your function is recursively calling itself, making time complexity analysis a bit harder.
Think about it this way: You generate all valid parenthesis of length n. It turns out that the number of all valid parenthesis of length n (taking your definition of n) is equal to the nth catalan number. Each string has length 2*n, So the time complexity is not a polynomial, but O(n*C(n)) where C(n) is the nth catalan number.
Edit: It seems like this question is already answered here.
Related
I am working through Cracking the Coding Interview, and I am unsure of an example on time-complexity. They provide this code to determine if a number is prime:
boolean isPrime(int n) {
for (int x = 2; x * x <= n; x++) {
if (n % x == 0) {
return false;
}
}
return true;
}
Later they say "The work inside the for loop is constant". Why is run-time for modulus operator constant? Why does it not depend on n?
The key part of the statement there is inside the for loop. All that's happening is a a modulo operation. Inside the function itself the time complexity depends on n
Try to understand running time of below algorithm for problem; Remove the minimum number of invalid parentheses in order to make the input string valid. Return all possible results
This is a simple BFS solution that generates all possible strings by removing "(" or ")".
public List<String> removeInvalidParentheses(String s) {
List<String> ret = new LinkedList<>();
Set<String> visited = new HashSet<>();
Queue<String> queue = new LinkedList<>();
queue.add(s);
while (!queue.isEmpty()) {
String current = queue.poll();
if (isValidParentheses(current)) {
ret.add(current);
}
if (ret.size() > 0) continue;
for (int i = 0; i < current.length(); i++) {
if (current.charAt(i) == '(' || current.charAt(i) == ')') {
String next = current.substring(0, i) + current.substring(i + 1);
if (!visited.contains(next)) {
visited.add(next);
queue.offer(next);
}
}
}
}
return ret;
}
public boolean isValidParentheses(String current) {
int open = 0;
int close = 0;
for (char c : current.toCharArray()) {
if (c == '(') open++;
else if (c == ')') close++;
if (close > open) return false;
}
return open == close;
}
It starts with generate n possible strings and next level it generate all strings with size n-1 length, and n-2 length, etc .. for )()( example
)()( len n
()( ))( ()( )() n-1
() (( () n-2
each level it checks all possible strings with n-level length.
given this - I was having hard to time figure out how to finalize the running time of this algorithm. How do I generalize this algorithm and analyze the complexity?
For the worst case, lets try with input as ((((. As per the logic above, it will push (((( in the queue, checking that this is invalid. So it would generate 4 more possible substrings of length 3 pushing them inside the queue. Again, on processing that queue elements, it would again generate more strings of length 2 for each substring of length 3, then for two and then end. We are assuming that T(1) = 1.
If you try to make a recurrence relation for the above logic, it would be
T(n) = nT(n-1) + 1, which can be written as
= `n((n-1)T(n-2) + 1) + 1` and so on.
On solving it completely, we would get T(n) = n! + n(n-1)/2 + 1 which would be O(n!).
Hence, I think the time complexity would be of order O(n!)
For more details on how to solve the recurrence relation T(n) = nT(n-1) + 1, please refer:
this post on its solution
So i've tried interpreting this pseudocode a friend made and i wasn't exactly sure that my method returns the right result. Anyone who's able to help me out?
I've done some test cases where e.g. an array of [2,0,7] or [0,1,4] or [0, 8, 0] would return true, but not cases like: [1,7,7] or [2,6,0].
Array(list, d)
for j = 0 to d−1 do
for i = 0 to d−1 do
for k = 0 to d−1 do
if list[j] + list[ i] + list[k] = 0 then
return true
end if
end for
end for
end for
return false
And i've made this in java:
public class One{
public static boolean method1(ArrayList<String> A, int a){
for(int i = 0; i < a-1; i++){
for(int j = 0; j < a-1; j++){
for(int k = 0; k < a-1; k++){
if(Integer.parseInt(A.get(i)+A.get(j)+A.get(k)) == 0){
return true;
}
}
}
}
return false;
}
}
Thanks in advance
For a fix to your concrete problem, see my comment. A nicer way to write that code would be to actually use a list of Integer instead of String, because you will then want to convert the strings back to integers. So, your method looks better like this:
public static boolean method(List<Integer> A) {
for (Integer i : A)
for (Integer j : A)
for (Integer k : A)
if (i + j + k == 0)
return true;
return false;
}
See that you don't even need the size as parameter, since any List in Java embeds its own size.
Somehow offtopic
You're probably trying to solve the following problem: "Find if a list of integers contains 3 different ones that sum up to 0". The solution to this problem doesn't have to be O(n^3), like yours, it can be solved in O(n^2). See this post.
Ok, so here is what I believe the pseudo code is trying to do. It returns true if there is a zero in your list or if there are three numbers that add up to zero in your list. So it should return true for following test cases. (0,1,2,3,4,5), (1,2,3,4,-3). It will return false for (1,2,3,4,5). I just used d=5 as a random example. Your code is good for the most part - you just need to add the ith, jth and kth elements in the list to check if their sum equals zero for the true condition.
I have a problem that I am unwilling to believe hasn't been solved before in Sage.
Given a pair of integers (d,n) as input, I'd like to receive a list (or set, or whatever) of all nondecreasing sequences of length d all of whose entries are no greater than n.
Similarly, I'd like another function which returns all strictly increasing sequences of length d whose entries are no greater than n.
For example, for d = 2 n=3, I'd receive the output:
[[1,2], [1,3], [2,3]]
or
[[1,1], [1,2], [1,3], [2,2], [2,3], [3,3]]
depending on whether I'm using increasing or nondecreasing.
Does anyone know of such a function?
Edit Of course, if there is such a method for nonincreasing or decreasing sequences, I can modify that to fit my purposes. Just something to iterate over sequences
I needed this algorithm too and I finally managed to write one today. I will share the code here, but I only started to learn coding last week, so it is not pretty.
Idea Input=(r,d). Step 1) Create a class "ListAndPosition" that has a list L of arrays Integer[r+1]'s, and an integer q between 0 and r. Step 2) Create a method that receives a ListAndPosition (L,q) and screens sequentially the arrays in L checking if the integer at position q is less than the one at position q+1, if so, it adds a new array at the bottom of the list with that entry ++. When done, the Method calls itself again with the new list and q-1 as input.
The code for Step 1)
import java.util.ArrayList;
public class ListAndPosition {
public static Integer r=5;
public final ArrayList<Integer[]> L;
public int q;
public ListAndPosition(ArrayList<Integer[]> L, int q) {
this.L = L;
this.q = q;
}
public ArrayList<Integer[]> getList(){
return L;
}
public int getPosition() {
return q;
}
public void decreasePosition() {
q--;
}
public void showList() {
for(int i=0;i<L.size();i++){
for(int j=0; j<r+1 ; j++){
System.out.print(""+L.get(i)[j]);
}
System.out.println("");
}
}
}
The code for Step 2)
import java.util.ArrayList;
public class NonDecreasingSeqs {
public static Integer r=5;
public static Integer d=3;
public static void main(String[] args) {
//Creating the first array
Integer[] firstArray;
firstArray = new Integer[r+1];
for(int i=0;i<r;i++){
firstArray[i] = 0;
}
firstArray[r] = d;
//Creating the starting listAndDim
ArrayList<Integer[]> L = new ArrayList<Integer[]>();
L.add(firstArray);
ListAndPosition Lq = new ListAndPosition(L,r-1);
System.out.println(""+nonDecSeqs(Lq).size());
}
public static ArrayList<Integer[]> nonDecSeqs(ListAndPosition Lq){
int iterations = r-1-Lq.getPosition();
System.out.println("How many arrays in the list after "+iterations+" iterations? "+Lq.getList().size());
System.out.print("Should we stop the iteration?");
if(0<Lq.getPosition()){
System.out.println(" No, position = "+Lq.getPosition());
for(int i=0;i<Lq.getList().size();i++){
//Showing particular array
System.out.println("Array of L #"+i+":");
for(int j=0;j<r+1;j++){
System.out.print(""+Lq.getList().get(i)[j]);
}
System.out.print("\nCan it be modified at position "+Lq.getPosition()+"?");
if(Lq.getList().get(i)[Lq.getPosition()]<Lq.getList().get(i)[Lq.getPosition()+1]){
System.out.println(" Yes, "+Lq.getList().get(i)[Lq.getPosition()]+"<"+Lq.getList().get(i)[Lq.getPosition()+1]);
{
Integer[] tempArray = new Integer[r+1];
for(int j=0;j<r+1;j++){
if(j==Lq.getPosition()){
tempArray[j] = new Integer(Lq.getList().get(i)[j])+1;
}
else{
tempArray[j] = new Integer(Lq.getList().get(i)[j]);
}
}
Lq.getList().add(tempArray);
}
System.out.println("New list");Lq.showList();
}
else{
System.out.println(" No, "+Lq.getList().get(i)[Lq.getPosition()]+"="+Lq.getList().get(i)[Lq.getPosition()+1]);
}
}
System.out.print("Old position = "+Lq.getPosition());
Lq.decreasePosition();
System.out.println(", new position = "+Lq.getPosition());
nonDecSeqs(Lq);
}
else{
System.out.println(" Yes, position = "+Lq.getPosition());
}
return Lq.getList();
}
}
Remark: I needed my sequences to start at 0 and end at d.
This is probably not a very good answer to your question. But you could, in principle, use Partitions and the max_slope=-1 argument. Messing around with filtering lists of IntegerVectors sounds equally inefficient and depressing for other reasons.
If this has a canonical name, it might be in the list of sage-combinat functionality, and there is even a base class you could perhaps use for integer lists, which is basically what you are asking about. Maybe you could actually get what you want using IntegerListsLex? Hope this proves helpful.
This question can be solved by using the class "UnorderedTuples" described here:
http://doc.sagemath.org/html/en/reference/combinat/sage/combinat/tuple.html
To return all all nondecreasing sequences with entries between 0 and n-1 of length d, you may type:
UnorderedTuples(range(n),d)
This returns the nondecreasing sequence as a list. I needed an immutable object (because the sequences would become keys of a dictionary). So I used the "tuple" method to turn the lists into tuples:
immutables = []
for s in UnorderedTuples(range(n),d):
immutables.append(tuple(s))
return immutables
And I also wrote a method which picks out only the increasing sequences:
def isIncreasing(list):
for i in range(len(list) - 1):
if list[i] >= list[i+1]:
return false
return true
The method that returns only strictly increasing sequences would look like
immutables = []
for s in UnorderedTuples(range(n),d):
if isIncreasing(s):
immutables.append(tuple(s))
return immutables
I was training in Codility solving the first lesson: Tape-Equilibrium.
It is said it has to be of complexity O(N). Therefore I was trying to solve the problem with just one for. I knew how to do it with two for but I understood it would have implied a complexity of O(2N), therefore I skipped those solutions.
I looked for it in Internet and of course, in SO there was an answer
To my astonishment, all the solutions first calculate the sum of the elements of the vector and afterwards make the calculations. I understand this is complexity O(2N), but it gets an score of 100%.
At this point, I think I am mistaken about my comprehension of the time complexity limits. If they ask you to get a time complexity of O(N), is it right to get O(X*N)? Being X a value not really high ?
How does this works?
Let f and g be functions.
The Big-O notation f in O(g) means that you can find a constant number c such that f(n) ≤ c⋅g(n). So if your algorithm has complexity 2N (or XN for a constant X) this is in O(N) due to c = 2 (or c = X) holds 2N ≤ c⋅N = 2⋅N (or XN ≤ c⋅N = X⋅N).
This is how I managed to keep it O(N) along with a 100% score:
// you can also use imports, for example:
// import java.util.*;
// you can use System.out.println for debugging purposes, e.g.
// System.out.println("this is a debug message");
class Solution {
public int solution(int[] A) {
int result = Integer.MAX_VALUE;
int[] s1 = new int[A.length-1];
int[] s2 = new int[A.length-1];
for(int i=0;i<A.length-1;i++){
if(i>0){
s1[i] = s1[i-1] + A[i];
}
else {
s1[i] = A[i];
}
}
for(int i=A.length-1;i>0;i--){
if(i<A.length-1){
s2[i-1] = s2[i] + A[i];
}
else {
s2[i-1] = A[A.length-1];
}
}
for(int i=0;i<A.length-1;i++){
if(Math.abs(s1[i]-s2[i])<result){
result = Math.abs(s1[i]-s2[i]);
}
}
return result;
}
}