I have to find the number of even number less than or equal to n having following conditions.
any even number can be written as (2^p)*k where p,k is some constant also it is given that n is also even number so n=(2^q)*m. Now how many even number less than or equal to n will satisfy p>q?
Well, my first thought was to implement it with a trivial solution, like the example:
const n = 1232;
function getPow(n) {
let tn = n, twoPow = 0;
while (tn % 2 === 0) {
tn /= 2;
twoPow++;
}
return twoPow;
}
const basePow = getPow(n);
let result = 0;
for (let i = 2; i < n; i+=2) {
const tmpPow = getPow(i);
if (tmpPow > basePow) {
result++;
}
}
console.log(result);
but then I realize that the solution is all the M numbers where ((q+1)^2)*x < n so this take you to the next formula, that basically is the solution:
const n = 1232;
function getPow(n) {
let tn = n, twoPow = 0;
while (tn % 2 === 0) {
tn /= 2;
twoPow++;
}
return twoPow;
}
console.log(Math.floor(n / (Math.pow(2 ,getPow(n) + 1))));
because the solution is O(log n) any limit fit in time
Related
I have got a code that generates all possible correct strings of balanced brackets. So if the input is n = 4 there should be 4 brackets in the string and thus the answers the code will give are: {}{} and
{{}}.
Now, what I would like to do is print the number of possible strings. For example, for n = 4 the outcome would be 2.
Given my code, is this possible and how would I make that happen?
Just introduce a counter.
// Change prototype to return the counter
int findBalanced(int p,int n,int o,int c)
{
static char str[100];
// The counter
static int count = 0;
if (c == n) {
// Increment it on every printout
count ++;
printf("%s\n", str);
// Just return zero. This is not used anyway and will give
// Correct result for n=0
return 0;
} else {
if (o > c) {
str[p] = ')';
findBalanced(p + 1, n, o, c + 1);
}
if (o < n) {
str[p] = '(';
findBalanced(p + 1, n, o + 1, c);
}
}
// Return it
return count;
}
What you're looking for is the n-th Catalan number. You'll need to implement binomial coefficient to calculate it, but that's pretty much it.
I'm trying to write a function to find the lowest number that all integers between 1 and 20 divide. (Let's call this Condition D)
Here's my solution, which is somehow exceeding the call stack size limit.
function findSmallest(num){
var count = 2
while (count<21){
count++
if (num % count !== 0){
// exit the loop
return findSmallest(num++)
}
}
return num
}
console.log(findSmallest(20))
Somewhere my reasoning on this is faulty but here's how I see it (please correct me where I'm wrong):
Calling this function with a number N that doesn't meet Condition D will result in the function being called again with N + 1. Eventually, when it reaches a number M that should satisfy Condition D, the while loop runs all the way through and the number M is returned by the function and there are no more recursive calls.
But I get this error on running it:
function findSmallest(num){
^
RangeError: Maximum call stack size exceeded
I know errors like this are almost always due to recursive functions not reaching a base case. Is this the problem here, and if so, where's the problem?
I found two bugs.
in your while loop, the value of count is 3 to 21.
the value of num is changed in loop. num++ should be num + 1
However, even if these bugs are fixed, the error will not be solved.
The answer is 232792560.
This recursion depth is too large, so stack memory exhausted.
For example, this code causes same error.
function foo (num) {
if (num === 0) return
else foo(num - 1)
}
foo(232792560)
Coding without recursion can avoid errors.
Your problem is that you enter the recursion more than 200 million times (plus the bug spotted in the previous answer). The number you are looking for is the multiple of all prime numbers times their max occurrences in each number of the defined range. So here is your solution:
function findSmallestDivisible(n) {
if(n < 2 || n > 100) {
throw "Numbers between 2 and 100 please";
}
var arr = new Array(n), res = 2;
arr[0] = 1;
arr[1] = 2;
for(var i = 2; i < arr.length; i++) {
arr[i] = fix(i, arr);
res *= arr[i];
}
return res;
}
function fix(idx, arr) {
var res = idx + 1;
for(var i = 1; i < idx; i++) {
if((res % arr[i]) == 0) {
res /= arr[i];
}
}
return res;
}
https://jsfiddle.net/7ewkeamL/
I'm trying to determine the big O complexity of several algorithms and I'm having trouble understanding how to reason about the following piece of code:
void recursiveFunc (n)
{
for(int i = 0; i < 8; i++)
{
if (n < maxN)
{
recursiveFunc (n + 1);
}
else
{
for(int j = 0; j < 8; j++)
{
// do some stuff
}
}
}
}
I have the idea this is an exponential since the recursive function is called inside the loop. O(8^n)?
But I'm a bit lost on how to reason about it.
Your tip is correct.
Given the code:
var maxN = 16;
var initN = 0;
var count = 0;
function recursiveFunc (n) {
for(var i = 0; i < 8; i++) {
if (n < maxN) {
recursiveFunc (n + 1);
} else {
for(int j = 0; j < 8; j++) {
count++;
}
}
}
}
recursiveFunc(initN);
First call happens with n = 0:
8^1 calls of recursiveFunc (where n = 1)
calls with n = 1 then cause: 8^2 calls of recursiveFunc
...
calls with n = (maxN - 1) then cause: 8^maxN calls of recursiveFunc => visiting else branch, each call enters "some stuff" 64 times
First call happens with n = 2, maxN = 4:
8^1 calls of recursiveFunc (where n = 3)
calls with n = 3 then cause: 8^2 calls of recursiveFunc (where n = 4)
calls with n = 4 then visit else branch, each 64 times.
Therefore total count of entering the "some stuff" stage: 64 * (8^(maxN - initN))
O(8^n)
EDIT: You can see this in work here, just click "Run the snippet" and then hit the "Test" button. (Trying bigger maxN values may crash your browser)
function test () {
var maxN = parseInt(document.querySelector("#nmax").value);
var initN = parseInt(document.querySelector("#n").value);
var count = 0;
function recursiveFunc (n) {
for(var i = 0; i < 8; i++) {
if (n < maxN) {
recursiveFunc (n + 1);
} else {
for(var j = 0; j < 8; j++) {
count++;
}
}
}
}
recursiveFunc(initN);
document.querySelector("#expectedValue").innerHTML = 64 * (Math.pow(8, maxN - initN));// 64 * (8^(maxN - initN));
document.querySelector("#realValue").innerHTML = count;
}
N: <input type=number id=n value=0><br>
NMAX: <input type=number id=nmax value=5><br>
<hr>
Expected value: <span id=expectedValue></span><br>
Real value: <span id=realValue></span><br>
<button onclick="test()">Test</button>
(I also assume that initN <= maxN)
I've recently come across the following problem:
Let say I have an vector of random length (L) of 0 and 1 randomly distributed (for example [0,1,1,1,0,0,1,0]), I need to split the vector in two sub-vector at index K so following conditions are valid:
the left sub-vector must contains the maximum number of elements from
K in reverse order such as the number of zeros must be greater or
equal to the number of 1s
the right sub vector must contains the maximum number of element starting from K+1 such as the number of 1s must be greater or equal to the number of zeros
For example, [1,1,1,1,1,1,1,1,1,0,1,0,0,0,0,0,0,0,0] the split is at index 9, left vector is [1,0], right vector [0,1]
I wrote the following solution but the complexity is O(L^2). I think there could be a solution with complexity of worst case O(L) but I cannot find anything that can help me. Any idea? Thanks
var max = 0;
var kMax = -1;
var firstZeroFound = false;
for (var i = 0; i < testVector.Length - 1; i++)
{
if (!firstZeroFound)
{
if (testVector[i]) continue;
firstZeroFound = true;
}
var maxZero = FindMax(testVector, i, -1, -1, false);
if (maxZero == 0) continue;
var maxOne = FindMax(testVector, i + 1, testVector.Length, 1, true);
if (maxOne == 0) continue;
if ((maxZero + maxOne) <= max)
continue;
max = maxOne + maxZero;
kMax = i;
if (max == testVector.Length)
break;
}
Console.Write("The result is {0}", kMax);
int FindMax(bool[] v, int start, int end, int increment, bool maximize)
{
var max = 0;
var sum = 0;
var count = 0;
var i = start;
while (i != end)
{
count++;
if (v[i])
sum++;
if (maximize)
{
if (sum * 2 >= count)
max = count;
}
else if (sum * 2 <= count)
{
max = count;
}
i += increment;
}
return max;
}
I think you should look at rle.
y <- c(1,1,1,1,1,1,1,1,1,0,1,0,0,0,0,0,0,0,0)
z <- rle(y)
d <- cbind(z$values, z$lengths)
[,1] [,2]
[1,] 1 9
[2,] 0 1
[3,] 1 1
[4,] 0 8
Basically, rle calculates the lengths of 0's and 1's at each level.
From here things may go easier for you.
As we all know, the simplest algorithm to generate Fibonacci sequence is as follows:
if(n<=0) return 0;
else if(n==1) return 1;
f(n) = f(n-1) + f(n-2);
But this algorithm has some repetitive calculation. For example, if you calculate f(5), it will calculate f(4) and f(3). When you calculate f(4), it will again calculate both f(3) and f(2). Could someone give me a more time-efficient recursive algorithm?
I have read about some of the methods for calculating Fibonacci with efficient time complexity following are some of them -
Method 1 - Dynamic Programming
Now here the substructure is commonly known hence I'll straightly Jump to the solution -
static int fib(int n)
{
int f[] = new int[n+2]; // 1 extra to handle case, n = 0
int i;
f[0] = 0;
f[1] = 1;
for (i = 2; i <= n; i++)
{
f[i] = f[i-1] + f[i-2];
}
return f[n];
}
A space-optimized version of above can be done as follows -
static int fib(int n)
{
int a = 0, b = 1, c;
if (n == 0)
return a;
for (int i = 2; i <= n; i++)
{
c = a + b;
a = b;
b = c;
}
return b;
}
Method 2- ( Using power of the matrix {{1,1},{1,0}} )
This an O(n) which relies on the fact that if we n times multiply the matrix M = {{1,1},{1,0}} to itself (in other words calculate power(M, n )), then we get the (n+1)th Fibonacci number as the element at row and column (0, 0) in the resultant matrix. This solution would have O(n) time.
The matrix representation gives the following closed expression for the Fibonacci numbers:
fibonaccimatrix
static int fib(int n)
{
int F[][] = new int[][]{{1,1},{1,0}};
if (n == 0)
return 0;
power(F, n-1);
return F[0][0];
}
/*multiplies 2 matrices F and M of size 2*2, and
puts the multiplication result back to F[][] */
static void multiply(int F[][], int M[][])
{
int x = F[0][0]*M[0][0] + F[0][1]*M[1][0];
int y = F[0][0]*M[0][1] + F[0][1]*M[1][1];
int z = F[1][0]*M[0][0] + F[1][1]*M[1][0];
int w = F[1][0]*M[0][1] + F[1][1]*M[1][1];
F[0][0] = x;
F[0][1] = y;
F[1][0] = z;
F[1][1] = w;
}
/*function that calculates F[][] raise to the power n and puts the
result in F[][]*/
static void power(int F[][], int n)
{
int i;
int M[][] = new int[][]{{1,1},{1,0}};
// n - 1 times multiply the matrix to {{1,0},{0,1}}
for (i = 2; i <= n; i++)
multiply(F, M);
}
This can be optimized to work in O(Logn) time complexity. We can do recursive multiplication to get power(M, n) in the previous method.
static int fib(int n)
{
int F[][] = new int[][]{{1,1},{1,0}};
if (n == 0)
return 0;
power(F, n-1);
return F[0][0];
}
static void multiply(int F[][], int M[][])
{
int x = F[0][0]*M[0][0] + F[0][1]*M[1][0];
int y = F[0][0]*M[0][1] + F[0][1]*M[1][1];
int z = F[1][0]*M[0][0] + F[1][1]*M[1][0];
int w = F[1][0]*M[0][1] + F[1][1]*M[1][1];
F[0][0] = x;
F[0][1] = y;
F[1][0] = z;
F[1][1] = w;
}
static void power(int F[][], int n)
{
if( n == 0 || n == 1)
return;
int M[][] = new int[][]{{1,1},{1,0}};
power(F, n/2);
multiply(F, F);
if (n%2 != 0)
multiply(F, M);
}
Method 3 (O(log n) Time)
Below is one more interesting recurrence formula that can be used to find nth Fibonacci Number in O(log n) time.
If n is even then k = n/2:
F(n) = [2*F(k-1) + F(k)]*F(k)
If n is odd then k = (n + 1)/2
F(n) = F(k)*F(k) + F(k-1)*F(k-1)
How does this formula work?
The formula can be derived from the above matrix equation.
fibonaccimatrix
Taking determinant on both sides, we get
(-1)n = Fn+1Fn-1 – Fn2
Moreover, since AnAm = An+m for any square matrix A, the following identities can be derived (they are obtained from two different coefficients of the matrix product)
FmFn + Fm-1Fn-1 = Fm+n-1
By putting n = n+1,
FmFn+1 + Fm-1Fn = Fm+n
Putting m = n
F2n-1 = Fn2 + Fn-12
F2n = (Fn-1 + Fn+1)Fn = (2Fn-1 + Fn)Fn (Source: Wiki)
To get the formula to be proved, we simply need to do the following
If n is even, we can put k = n/2
If n is odd, we can put k = (n+1)/2
public static int fib(int n)
{
if (n == 0)
return 0;
if (n == 1 || n == 2)
return (f[n] = 1);
// If fib(n) is already computed
if (f[n] != 0)
return f[n];
int k = (n & 1) == 1? (n + 1) / 2
: n / 2;
// Applyting above formula [See value
// n&1 is 1 if n is odd, else 0.
f[n] = (n & 1) == 1? (fib(k) * fib(k) +
fib(k - 1) * fib(k - 1))
: (2 * fib(k - 1) + fib(k))
* fib(k);
return f[n];
}
Method 4 - Using a formula
In this method, we directly implement the formula for the nth term in the Fibonacci series. Time O(1) Space O(1)
Fn = {[(√5 + 1)/2] ^ n} / √5
static int fib(int n) {
double phi = (1 + Math.sqrt(5)) / 2;
return (int) Math.round(Math.pow(phi, n)
/ Math.sqrt(5));
}
Reference: http://www.maths.surrey.ac.uk/hosted-sites/R.Knott/Fibonacci/fibFormula.html
Look here for implementation in Erlang which uses formula
. It shows nice linear resulting behavior because in O(M(n) log n) part M(n) is exponential for big numbers. It calculates fib of one million in 2s where result has 208988 digits. The trick is that you can compute exponentiation in O(log n) multiplications using (tail) recursive formula (tail means with O(1) space when used proper compiler or rewrite to cycle):
% compute X^N
power(X, N) when is_integer(N), N >= 0 ->
power(N, X, 1).
power(0, _, Acc) ->
Acc;
power(N, X, Acc) ->
if N rem 2 =:= 1 ->
power(N - 1, X, Acc * X);
true ->
power(N div 2, X * X, Acc)
end.
where X and Acc you substitute with matrices. X will be initiated with and Acc with identity I equals to .
One simple way is to calculate it iteratively instead of recursively. This will calculate F(n) in linear time.
def fib(n):
a,b = 0,1
for i in range(n):
a,b = a+b,a
return a
Hint: One way you achieve faster results is by using Binet's formula:
Here is a way of doing it in Python:
from decimal import *
def fib(n):
return int((Decimal(1.6180339)**Decimal(n)-Decimal(-0.6180339)**Decimal(n))/Decimal(2.236067977))
you can save your results and use them :
public static long[] fibs;
public long fib(int n) {
fibs = new long[n];
return internalFib(n);
}
public long internalFib(int n) {
if (n<=2) return 1;
fibs[n-1] = fibs[n-1]==0 ? internalFib(n-1) : fibs[n-1];
fibs[n-2] = fibs[n-2]==0 ? internalFib(n-2) : fibs[n-2];
return fibs[n-1]+fibs[n-2];
}
F(n) = (φ^n)/√5 and round to nearest integer, where φ is the golden ratio....
φ^n can be calculated in O(lg(n)) time hence F(n) can be calculated in O(lg(n)) time.
// D Programming Language
void vFibonacci ( const ulong X, const ulong Y, const int Limit ) {
// Equivalent : if ( Limit != 10 ). Former ( Limit ^ 0xA ) is More Efficient However.
if ( Limit ^ 0xA ) {
write ( Y, " " ) ;
vFibonacci ( Y, Y + X, Limit + 1 ) ;
} ;
} ;
// Call As
// By Default the Limit is 10 Numbers
vFibonacci ( 0, 1, 0 ) ;
EDIT: I actually think Hynek Vychodil's answer is superior to mine, but I'm leaving this here just in case someone is looking for an alternate method.
I think the other methods are all valid, but not optimal. Using Binet's formula should give you the right answer in principle, but rounding to the closest integer will give some problems for large values of n. The other solutions will unnecessarily recalculate the values upto n every time you call the function, and so the function is not optimized for repeated calling.
In my opinion the best thing to do is to define a global array and then to add new values to the array IF needed. In Python:
import numpy
fibo=numpy.array([1,1])
last_index=fibo.size
def fib(n):
global fibo,last_index
if (n>0):
if(n>last_index):
for i in range(last_index+1,n+1):
fibo=numpy.concatenate((fibo,numpy.array([fibo[i-2]+fibo[i-3]])))
last_index=fibo.size
return fibo[n-1]
else:
print "fib called for index less than 1"
quit()
Naturally, if you need to call fib for n>80 (approximately) then you will need to implement arbitrary precision integers, which is easy to do in python.
This will execute faster, O(n)
def fibo(n):
a, b = 0, 1
for i in range(n):
if i == 0:
print(i)
elif i == 1:
print(i)
else:
temp = a
a = b
b += temp
print(b)
n = int(input())
fibo(n)