I need a task about finding Fibonacci Sequence for my independent project in Java. Here are methods for find.
private static long getFibonacci(int n) {
switch (n) {
case 0:
return 0;
case 1:
return 1;
default:
return (getFibonacci(n-1)+getFibonacci(n-2));
}
}
private static long getFibonacciSum(int n) {
long result = 0;
while(n >= 0) {
result += getFibonacci(n);
n--;
}
return result;
}
private static boolean isInFibonacci(long n) {
long a = 0, b = 1, c = 0;
while (c < n) {
c = a + b;
a = b;
b = c;
}
return c == n;
}
Here is main method:
long key = getFibonacciSum(n);
System.out.println("Sum of all Fibonacci Numbers until Fibonacci[n]: "+key);
System.out.println(getFibonacci(n)+" is Fibonacci[n]");
System.out.println("Is n2 in Fibonacci Sequence ?: "+isInFibonacci(n2));
Codes are completely done and working. But if the n or n2 will be more than normal (50th numbers in Fib. Seq.) ? Codes will be runout. Are there any suggestions ?
There is a way to calculate Fibonacci numbers instantaneously by using Binet's Formula
Algorithm:
function fib(n):
root5 = squareroot(5)
gr = (1 + root5) / 2
igr = 1 - gr
value = (power(gr, n) - power(igr, n)) / root5
// round it to the closest integer since floating
// point arithmetic cannot be trusted to give
// perfect integer answers.
return floor(value + 0.5)
Once you do this, you need to be aware of the programming language you're using and how it behaves. This will probably return a floating point decimal type, whereas integers are probably desired.
The complexity of this solution is O(1).
Yes, one improvement you can do is to getFibonacciSum(): instead of calling again and again to isInFibonacci which re-calculates everything from scratch, you can do the exact same thing that isInFibonacci is doing and get the sum in one pass, something like:
private static int getFibonacciSum(int n) {
int a = 0, b = 1, c = 0, sum = 0;
while (c < n) {
c = a + b;
a = b;
sum += b;
b = c;
}
sum += c;
return sum;
}
Well, here goes my solution using a Map and some math formulas. (source:https://www.nayuki.io/page/fast-fibonacci-algorithms)
F(2k) = F(k)[2F(k+1)−F(k)]
F(2k+1) = F(k+1)^2+F(k)^2
It is also possible implement it using lists instead of a map but it is just reinventing the wheel.
When using Iteration solution, we don't worry about running out of memory, but it takes a lot of time to get fib(1000000), for example. In this solution we may be running out of memory for very very very very big inputs (like 10000 billion, idk) but it is much much much faster.
public BigInteger fib(BigInteger n) {
if (n.equals(BigInteger.ZERO))
return BigInteger.ZERO;
if (n.equals(BigInteger.ONE) || n.equals(BigInteger.valueOf(2)))
return BigInteger.ONE;
BigInteger index = n;
//we could have 2 Lists instead of a map
Map<BigInteger,BigInteger> termsToCalculate = new TreeMap<BigInteger,BigInteger>();
//add every index needed to calculate index n
populateMapWhitTerms(termsToCalculate, index);
termsToCalculate.put(n,null); //finally add n to map
Iterator<Map.Entry<BigInteger, BigInteger>> it = termsToCalculate.entrySet().iterator();//it
it.next(); //it = key number 1, contains fib(1);
it.next(); //it = key number 2, contains fib(2);
//map is ordered
while (it.hasNext()) {
Map.Entry<BigInteger, BigInteger> pair = (Entry<BigInteger, BigInteger>)it.next();//first it = key number 3
index = (BigInteger) pair.getKey();
if(index.remainder(BigInteger.valueOf(2)).equals(BigInteger.ZERO)) {
//index is divisible by 2
//F(2k) = F(k)[2F(k+1)−F(k)]
pair.setValue(termsToCalculate.get(index.divide(BigInteger.valueOf(2))).multiply(
(((BigInteger.valueOf(2)).multiply(
termsToCalculate.get(index.divide(BigInteger.valueOf(2)).add(BigInteger.ONE)))).subtract(
termsToCalculate.get(index.divide(BigInteger.valueOf(2)))))));
}
else {
//index is odd
//F(2k+1) = F(k+1)^2+F(k)^2
pair.setValue((termsToCalculate.get(index.divide(BigInteger.valueOf(2)).add(BigInteger.ONE)).multiply(
termsToCalculate.get(index.divide(BigInteger.valueOf(2)).add(BigInteger.ONE)))).add(
(termsToCalculate.get(index.divide(BigInteger.valueOf(2))).multiply(
termsToCalculate.get(index.divide(BigInteger.valueOf(2))))))
);
}
}
// fib(n) was calculated in the while loop
return termsToCalculate.get(n);
}
private void populateMapWhitTerms(Map<BigInteger, BigInteger> termsToCalculate, BigInteger index) {
if (index.equals(BigInteger.ONE)) { //stop
termsToCalculate.put(BigInteger.ONE, BigInteger.ONE);
return;
} else if(index.equals(BigInteger.valueOf(2))){
termsToCalculate.put(BigInteger.valueOf(2), BigInteger.ONE);
return;
} else if(index.remainder(BigInteger.valueOf(2)).equals(BigInteger.ZERO)) {
// index is divisible by 2
// FORMUMA: F(2k) = F(k)[2F(k+1)−F(k)]
// add F(k) key to termsToCalculate (the key is replaced if it is already there, we are working with a map here)
termsToCalculate.put(index.divide(BigInteger.valueOf(2)), null);
populateMapWhitTerms(termsToCalculate, index.divide(BigInteger.valueOf(2)));
// add F(k+1) to termsToCalculate
termsToCalculate.put(index.divide(BigInteger.valueOf(2)).add(BigInteger.ONE), null);
populateMapWhitTerms(termsToCalculate, index.divide(BigInteger.valueOf(2)).add(BigInteger.ONE));
} else {
// index is odd
// FORMULA: F(2k+1) = F(k+1)^2+F(k)^2
// add F(k+1) to termsToCalculate
termsToCalculate.put(((index.subtract(BigInteger.ONE)).divide(BigInteger.valueOf(2)).add(BigInteger.ONE)),null);
populateMapWhitTerms(termsToCalculate,((index.subtract(BigInteger.ONE)).divide(BigInteger.valueOf(2)).add(BigInteger.ONE)));
// add F(k) to termsToCalculate
termsToCalculate.put((index.subtract(BigInteger.ONE)).divide(BigInteger.valueOf(2)), null);
populateMapWhitTerms(termsToCalculate, (index.subtract(BigInteger.ONE)).divide(BigInteger.valueOf(2)));
}
}
This method of solution is called dynamic programming
In this method we are remembering the previous results
so when recursion happens then the cpu doesn't have to do any work to recompute the same value again and again
class fibonacci
{
static int fib(int n)
{
/* Declare an array to store Fibonacci numbers. */
int f[] = new int[n+1];
int i;
/* 0th and 1st number of the series are 0 and 1*/
f[0] = 0;
f[1] = 1;
for (i = 2; i <= n; i++)
{
/* Add the previous 2 numbers in the series
and store it */
f[i] = f[i-1] + f[i-2];
}
return f[n];
}
public static void main (String args[])
{
int n = 9;
System.out.println(fib(n));
}
}
public static long getFib(final int index) {
long a=0,b=0,total=0;
for(int i=0;i<= index;i++) {
if(i==0) {
a=0;
total=a+b;
}else if(i==1) {
b=1;
total=a+b;
}
else if(i%2==0) {
total = a+b;
a=total;
}else {
total = a+b;
b=total;
}
}
return total;
}
I have checked all solutions and for me, the quickest one is to use streams and this code could be easily modified to collect all Fibonacci numbers.
public static Long fibonaciN(long n){
return Stream.iterate(new long[]{0, 1}, a -> new long[]{a[1], a[0] + a[1]})
.limit(n)
.map(a->a[0])
.max(Long::compareTo)
.orElseThrow();
}
50 or just below 50 is as far as you can go with straight recursive implementation. You can switch to iterative or dynamic programming (DP) approaches if you want to go much higher than that. I suggest learning about those from this: https://www.javacodegeeks.com/2014/02/dynamic-programming-introduction.html. And don't forget to look the a solution in the comment by David therein, real efficient. The links shows how even n = 500000 can be computed instantaneously using the DP method. The link also explains the concept of "memoization" to speed up computation by storing intermediate (but later on re-callable) results.
Related
The Problem
Given a set of integers, find a subset of those integers which sum to 100,000,000.
Solution
I am attempting to build a tree containing all the combinations of the given set along with the sum. For example, if the given set looked like 0,1,2, I would build the following tree, checking the sum at each node:
{}
{} {0}
{} {1} {0} {0,1}
{} {2} {1} {1,2} {0} {2} {0,1} {0,1,2}
Since I keep both the array of integers at each node and the sum, I should only need the bottom (current) level of the tree in memory.
Issues
My current implementation will maintain the entire tree in memory and therefore uses way too much heap space.
How can I change my current implementation so that the GC will take care of my upper tree levels?
(At the moment I am just throwing a RuntimeException when I have found the target sum but this is obviously just for playing around)
public class RecursiveSolver {
static final int target = 100000000;
static final int[] set = new int[]{98374328, 234234123, 2341234, 123412344, etc...};
Tree initTree() {
return nextLevel(new Tree(null), 0);
}
Tree nextLevel(Tree currentLocation, int current) {
if (current == set.length) { return null; }
else if (currentLocation.sum == target) throw new RuntimeException(currentLocation.getText());
else {
currentLocation.left = nextLevel(currentLocation.copy(), current + 1);
Tree right = currentLocation.copy();
right.value = add(currentLocation.value, set[current]);
right.sum = currentLocation.sum + set[current];
currentLocation.right = nextLevel(right, current + 1);
return currentLocation;
}
}
int[] add(int[] array, int digit) {
if (array == null) {
return new int[]{digit};
}
int[] newValue = new int[array.length + 1];
for (int i = 0; i < array.length; i++) {
newValue[i] = array[i];
}
newValue[array.length] = digit;
return newValue;
}
public static void main(String[] args) {
RecursiveSolver rs = new RecursiveSolver();
Tree subsetTree = rs.initTree();
}
}
class Tree {
Tree left;
Tree right;
int[] value;
int sum;
Tree(int[] value) {
left = null;
right = null;
sum = 0;
this.value = value;
if (value != null) {
for (int i = 0; i < value.length; i++) sum += value[i];
}
}
Tree copy() {
return new Tree(this.value);
}
}
The time and space you need for building the tree here is absolutely nothing at all.
The reason is because, if you're given
A node of the tree
The depth of the node
The ordered array of input elements
you can simply compute its parent, left, and right children nodes using O(1) operations. And you have access to each of those things while you're traversing the tree, so you don't need anything else.
The problem is NP-complete.
If you really want to improve performance, then you have to forget about your tree implementation. You either have to just generate all the subsets and sum them up or to use dynamic programming.
The choice depends on the number of elements to sum and the sum you want to achieve. You know the sum it is 100,000,000, bruteforce exponential algorithm runs in O(2^n * n) time, so for number below 22 it makes sense.
In python you can achieve this with a simple:
def powerset(iterable):
"powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))
You can significantly improve this complexity (sacrificing the memory) by using meet in the middle technique (read the wiki article). This will decrease it to O(2^(n/2)), which means that it will perform better than DP solution for n <~ 53
After thinking more about erip's comments, I realized he is correct - I shouldn't be using a tree to implement this algorithm.
Brute force usually is O(n*2^n) because there are n additions for 2^n subsets. Because I only do one addition per node, the solution I came up with is O(2^n) where n is the size of the given set. Also, this algorithm is only O(n) space complexity. Since the number of elements in the original set in my particular problem is small (around 25) O(2^n) complexity is not too much of a problem.
The dynamic solution to this problem is O(t*n) where t is the target sum and n is the number of elements. Because t is very large in my problem, the dynamic solution ends up with a very long runtime and a high memory usage.
This completes my particular solution in around 311 ms on my machine, which is a tremendous improvement over the dynamic programming solutions I have seen for this particular class of problem.
public class TailRecursiveSolver {
public static void main(String[] args) {
final long starttime = System.currentTimeMillis();
try {
step(new Subset(null, 0), 0);
}
catch (RuntimeException ex) {
System.out.println(ex.getMessage());
final long endtime = System.currentTimeMillis();
System.out.println(endtime - starttime);
}
}
static final int target = 100000000;
static final int[] set = new int[]{ . . . };
static void step(Subset current, int counter) {
if (current.sum == target) throw new RuntimeException(current.getText());
else if (counter == set.length) {}
else {
step(new Subset(add(current.subset, set[counter]), current.sum + set[counter]), counter + 1);
step(current, counter + 1);
}
}
static int[] add(int[] array, int digit) {
if (array == null) {
return new int[]{digit};
}
int[] newValue = new int[array.length + 1];
for (int i = 0; i < array.length; i++) {
newValue[i] = array[i];
}
newValue[array.length] = digit;
return newValue;
}
}
class Subset {
int[] subset;
int sum;
Subset(int[] subset, int sum) {
this.subset = subset;
this.sum = sum;
}
public String getText() {
String ret = "";
for (int i = 0; i < (subset == null ? 0 : subset.length); i++) {
ret += " + " + subset[i];
}
if (ret.startsWith(" ")) {
ret = ret.substring(3);
ret = ret + " = " + sum;
} else ret = "null";
return ret;
}
}
EDIT -
The above code still runs in O(n*2^n) time - since the add method runs in O(n) time. This following code will run in true O(2^n) time, and is MUCH more performant, completing in around 20 ms on my machine.
It is limited to sets less than 64 elements due to storing the current subset as the bits in a long.
public class SubsetSumSolver {
static boolean found = false;
static final int target = 100000000;
static final int[] set = new int[]{ . . . };
public static void main(String[] args) {
step(0,0,0);
}
static void step(long subset, int sum, int counter) {
if (sum == target) {
found = true;
System.out.println(getText(subset, sum));
}
else if (!found && counter != set.length) {
step(subset + (1 << counter), sum + set[counter], counter + 1);
step(subset, sum, counter + 1);
}
}
static String getText(long subset, int sum) {
String ret = "";
for (int i = 0; i < 64; i++) if((1 & (subset >> i)) == 1) ret += " + " + set[i];
if (ret.startsWith(" ")) ret = ret.substring(3) + " = " + sum;
else ret = "null";
return ret;
}
}
EDIT 2 -
Here is another version uses a meet in the middle attack, along with a little bit shifting in order to reduce the complexity from O(2^n) to O(2^(n/2)).
If you want to use this for sets with between 32 and 64 elements, you should change the int which represents the current subset in the step function to a long although performance will obviously drastically decrease as the set size increases. If you want to use this for a set with odd number of elements, you should add a 0 to the set to make it even numbered.
import java.util.ArrayList;
import java.util.List;
public class SubsetSumMiddleAttack {
static final int target = 100000000;
static final int[] set = new int[]{ ... };
static List<Subset> evens = new ArrayList<>();
static List<Subset> odds = new ArrayList<>();
static int[][] split(int[] superSet) {
int[][] ret = new int[2][superSet.length / 2];
for (int i = 0; i < superSet.length; i++) ret[i % 2][i / 2] = superSet[i];
return ret;
}
static void step(int[] superSet, List<Subset> accumulator, int subset, int sum, int counter) {
accumulator.add(new Subset(subset, sum));
if (counter != superSet.length) {
step(superSet, accumulator, subset + (1 << counter), sum + superSet[counter], counter + 1);
step(superSet, accumulator, subset, sum, counter + 1);
}
}
static void printSubset(Subset e, Subset o) {
String ret = "";
for (int i = 0; i < 32; i++) {
if (i % 2 == 0) {
if ((1 & (e.subset >> (i / 2))) == 1) ret += " + " + set[i];
}
else {
if ((1 & (o.subset >> (i / 2))) == 1) ret += " + " + set[i];
}
}
if (ret.startsWith(" ")) ret = ret.substring(3) + " = " + (e.sum + o.sum);
System.out.println(ret);
}
public static void main(String[] args) {
int[][] superSets = split(set);
step(superSets[0], evens, 0,0,0);
step(superSets[1], odds, 0,0,0);
for (Subset e : evens) {
for (Subset o : odds) {
if (e.sum + o.sum == target) printSubset(e, o);
}
}
}
}
class Subset {
int subset;
int sum;
Subset(int subset, int sum) {
this.subset = subset;
this.sum = sum;
}
}
I've been trying to implement Euclid's algorithm in Java for 2 numbers or more.The problem with my code is that
a) It works fine for 2 numbers,but returns the correct value multiple times when more than 2 numbers are entered.My guess is that this is probably because of the return statements in my code.
b) I don't quite understand how it works.Though I coded it myself,I don't quite understand how the return statements are working.
import java.util.*;
public class GCDCalc {
static int big, small, remainder, gcd;
public static void main(String[] args) {
Scanner sc = new Scanner(System.in);
// Remove duplicates from the arraylist containing the user input.
ArrayList<Integer> listofnum = new ArrayList();
System.out.println("GCD Calculator");
System.out.println("Enter the number of values you want to calculate the GCD of: ");
int counter = sc.nextInt();
for (int i = 0; i < counter; i++) {
System.out.println("Enter #" + (i + 1) + ": ");
int val = sc.nextInt();
listofnum.add(val);
}
// Sorting algorithm.
// This removed the need of conditional statements(we don't have to
// check if the 1st number is greater than the 2nd element
// before applying Euclid's algorithm.
// The outer loop ensures that the maximum number of swaps are occurred.
// It ensures the implementation of the swapping process as many times
// as there are numbers in the array.
for (int i = 0; i < listofnum.size(); i++) {
// The inner loop performs the swapping.
for (int j = 1; j < listofnum.size(); j++) {
if (listofnum.get(j - 1) > listofnum.get(j)) {
int dummyvar = listofnum.get(j);
int dummyvar2 = listofnum.get(j - 1);
listofnum.set(j - 1, dummyvar);
listofnum.set(j, dummyvar2);
}
}
}
// nodup contains the array containing the userinput,without any
// duplicates.
ArrayList<Integer> nodup = new ArrayList();
// Remove duplicates.
for (int i = 0; i < listofnum.size(); i++) {
if (!nodup.contains(listofnum.get(i))) {
nodup.add(listofnum.get(i));
}
}
// Since the array is sorted in ascending order,we can easily determine
// which of the indexes has the bigger and smaller values.
small = nodup.get(0);
big = nodup.get(1);
remainder = big % small;
if (nodup.size() == 2) {
recursion(big, small, remainder);
} else if (nodup.size() > 2) {
largerlist(nodup, big, small, 2);
} else // In the case,the array only consists of one value.
{
System.out.println("GCD: " + nodup.get(0));
}
}
// recursive method.
public static int recursion(int big, int small, int remainder) {
remainder = big % small;
if (remainder == 0) {
System.out.println(small);
} else {
int dummyvar = remainder;
big = small;
small = dummyvar;
recursion(big, small, remainder);
}
return small;
}
// Method to deal with more than 2 numbers.
public static void largerlist(ArrayList<Integer> list, int big, int small, int counter) {
remainder = big % small;
gcd = recursion(big, small, remainder);
if (counter == list.size()) {
} else if (counter != list.size()) {
big = gcd;
small = list.get(counter);
counter++;
largerlist(list, gcd, small, counter);
}
}
}
I apologize in advance for any formatting errors etc.
Any suggestions would be appreciated.Thanks!
I think these two assignments are the wrong way around
big = gcd;
small = list.get(counter);
and then big not used
largerlist(list, gcd, small, counter);
Also you've used static variables, which is usually a problem.
I suggest removing static/global variables and generally don't reuse variables.
Edit: Oh yes, return. You've ignored the return value of the recursion method when called from the recursion method. That shouldn't matter as you are printing out instead of returning the value, but such solutions break when, say, you want to use the function more than once.
I seem to be having an issue regarding implementing a power set algorithm using backtrack. What I am trying to achieve is rather simple, generate the power set of any given numbers:
Ex. [1 2 3] => [1] [2] [3] ; [1,2] [1,3] [2,3] ; [1,2,3]
My algorithm is using a stack to place the numbers, it adds the numbers to the stack and sends them for calculations. The code is as follows:
public int calculatePowerSet(int x, LinkedList<Integer> arr)
{
int size = 1;
int nrOfTimes=0;
int calculate =0;
boolean goOn=true;
Stack<Integer> stack = new Stack<Integer>();
int k=0, len = arr.size();
double temp=0.0f;
while(size<=len)
{
goOn=true;
stack.push(arr.get(0));
k = arr.indexOf(stack.peek());
temp = size; //ignore these as they are for calculating time
temp/=len; //ignore these as they are for calculating time
temp*=100; //ignore these as they are for calculating time
setPowerSetPrecentage((int)temp);
while(goOn)
{
if(isStopProcess())return 0;
if((k==len)&&(stack.size()==0)) goOn=false;
else if(stack.size()==size)
{
String sign = "";
if((stack.size()%2)==0) sign="+";
else sign="-";
calculate =calculateSets(stack.toArray(), sign, calculate, x);
k = arr.indexOf(stack.pop())+1;
}
else if(k==len)
k = arr.indexOf(stack.pop())+1;
else
{
prepereStack(stack,arr.get(k));
k++;
}
}
size++;
}
return calculate;
}
Here is the calculate method:
private int calculate(int[] arr2, int x)
{
int calc=1;
float rez = 0;
for(int i=0;i<arr2.length;i++)
calc*=arr2[i];
rez = (float)(x/calc);
calc = (int) (rez+0.5d);
return calc;
}
The code seems to be working perfectly for all numbers bellow 20, but after that i seem to be getting wrong results. I cannot check manually through the numbers as there are hundreds of combinations. For example for one input of 25 numbers i should get a result of 1229, instead i get 1249. I am not sure what i am missing as i think the algorithm should be working in theory, so if anyone has any suggestions that would be great.
I would recommend separating out the generation of the power sets from your calculation. While there are some very efficient algorithms for generating power sets I would suggest keeping it quite simple until you need the efficiency.
private void forEachSet(List<Integer> currentSet, List<Integer> rest) {
if (rest.isEmpty()) {
process(currentSet);
} else {
Integer nextInt = rest.remove(0);
forEachSet(currentSet, rest);
currentSet.add(nextInt);
forEachSet(currentSet, rest);
current.remove(nextInt);
rest.add(nextInt);
}
}
public forEachSet(List<Integer> set) {
forEachSet(new ArrayList<>(), new ArrayList<>(set));
}
As the title reads, I have been thinking about creating multiple nested loops that aim to achieve one purpose. Move two generated random numbers between 0-9 through each possible possition of an array.
For example, App generates first number (fNum) 1 and second number (sNum) 6. It then moves these numbers in the array which containts ABC. However firstNum and secondNum will need to also try all the possible combinations, so each one will need to be different with each loop.
-1ABC6
-A1BC6
-AB1C6
-ABC16
-ABC61
-AB6C1
-A6BC1
-6ABC1
-A6B1C
-A61BC
-A16BC
-A1B6C
-A1BC6
and so on...
I beleive the best way will be to create a method for generating a counter, which increments the numbers which I can call.
private int getNextNumber(int num) {
if (num == 0) {
return num;
} else {
num++;
}
if (num < 10) {
return num;
} else {
return -1;
}
}
Then I will need multiple nested loops... I have decided to go for several loops which will go infinitly.
while (j < maxlen) {
//J = 0 and maxlen = length of text so in this case 3 as it is ABC
//Add two numbers and check against answer
while (fNum != -1 || sNum != -1) {
//incrememnt numbers
fNum = getNextNumber(fNum);
System.out.println(fNum);
sNum = getNextNumber(sNum);
System.out.println(fNum);
}
String textIni = "ABC";
int lenOfText = textIni.length();
char[] split = textIni.toCharArray();
for (int i = 0; i < lenOfText; i++) {
//here it will look at the length of the Text and
//try the possible positions it could be at....
//maybe wiser to do a longer loop but I am not too sure
}
}
Since you don't need to store all possible combinations, we will save some memory using only O(n) storage with an iterative solution. I propose you a basic implementation but don't expect to use it on large arrays since it has a O(n³) complexity.
public static void generateCombinationsIterative(List<Integer> original, int fnum, int snum) {
int size = original.size();
for (int i=0 ; i<=size ; i++) {
List<Integer> tmp = new ArrayList<>(original);
tmp.add(i,fnum);
for (int j=0 ; j<=size + 1 ; j++) {
tmp.add(j,snum);
System.out.print(tmp + (i == size && j == size + 1 ? "" : ", "));
tmp.remove(j);
}
}
}
For your culture, here is an example of a recursive solution, which takes a lot of memory so don't use it if you don't need to generate the lists of results. Nevertheless, this is a more general solution that can deal with any number of elements to insert.
public static List<List<Integer>> generateCombinations(List<Integer> original, Deque<Integer> toAdd) {
if (toAdd.isEmpty()) {
List<List<Integer>> res = new ArrayList<>();
res.add(original);
return res;
}
int element = toAdd.pop();
List<List<Integer>> res = new LinkedList<>();
for (int i=0 ; i<=original.size() ; i++)
// you must make a copy of toAdd, otherwise each recursive call will perform
// a pop() on it and the result will be wrong
res.addAll(generateCombinations(insertAt(original,element,i),new LinkedList<>(toAdd)));
return res;
}
// a helper function for a clear code
public static List<Integer> insertAt(List<Integer> input, int element, int index) {
List<Integer> result = new ArrayList<>(input);
result.add(index,element);
return result;
}
Note that I did not use any array in order to benefit from dynamic data structures, however you can call the methods like this :
int[] arr = { 1,2,3 };
int fnum = 4, snum = 5;
generateCombinationsIterative(Arrays.asList(arr),fnum,snum);
generateCombinations(Arrays.asList(arr),new LinkedList<>(Arrays.asList(fnum,snum));
Note that both methods generate the combinations in the same order.
I want to find the Nth number of the Recurrence Equation
T(n)=T(n-1)+3T(n-2)+3T(n-3)+(n-4),T(1)=T(4)=1,T(2)=T(3)=3
so if suppose you entered 2,5,9 as input, output should be T(2)=3,T(5)=20,T(9)=695
what I did is create an array of size equal to maximum of all input value and storing solution of T(i) at index i.Then look up into the array for specific index. eg array[3] for T(3),array[5] for T(5),etc
The code worked fine till maximum number is not greater than maximum integer value system can hold i.e
Integer.MAXValue.
Because the index of array can only be integer then
if number is n=1855656959555656 what should be the best way to find the solution of
T(1855656959555656)?
as clearly I cant create an array of size=1855656959555656..
I have even tried BigInteger from java.Math but with no success.
I have to find some other approach.please suggest some ideas..
Thanks
you do not need to store every T(i), you only need to store 3 values T(i-1), T(i-2), T(i-3). While looping over i, check if the current i should be part of your output, if so put it out immediately or save it to an "output"-array.
edit: this part is quite inefficient. You check in every iteation EVERY needed output.
for (int k = 0; k < arr.length; ++k) {
if (count == arr[k])
T[k] = temp[i];
else if (arr[k] == 1)
T[k] = 1;
else if (arr[k] == 2)
T[k] = 3;
else if (arr[k] == 3)
T[k] = 3;
else if (arr[k] == 4)
T[k] = 1;
}
so your code runs in time (max*arr.length) you can reduce it to only (max). Use a HashMap with key=neededPosition (=count) value=position in arr
Init the map like this:
Map<Long, Integer> map = new HashMap<Long, Integer>();
for (int i = 0; i < arr.length; i++) {
map.put(arr[i], i);
}
if (map.containsKey(count)) {
T[map.get(count)] = temp[i]
}
check the values 1-4 just once after the whole thing!
Not possible. The array size can be a maximum of Integer.MAX_VALUE (minus something usually 5 or 8, depending on the JVM capabilities). Why?. The index for an Array should be an integer thats a limitation.
It can't be done. So you need to solve the problem by introducing a sharding mechanism. The simplest way would be to just have arrays of arrays with a fixed length.
Edit: You really do not need this much storage for your problem at hand (as pointed out in another answer; this code fragment avoids arrays altogether to avoid bounds checks / indirection):
public void t(long n) {
if (n < 5) {
return (n == 2 || n == 3) ? 3 : 1;
}
long i = 5; // Initialize variables for n == 5;
long tn_1 = 1; // T(n-1) = T(4) = 1;
long tn_2 = 3; // T(n-2) = T(3) = 3;
long tn_3 = 1; // T(n-3) = T(2) = 1;
long tn_4 = 3; // T(n-4) = T(1) = 3;
while (true) {
long tn = tn_1 + 3*tn_2 + 3*tn_3 + tn_4;
if (i++ == n) {
return tn;
}
tn_4 = tn_3;
tn_3 = tn_2;
tn_2 = tn_1;
tn_1 = tn;
}
}
To answer the question in the title anyway:
If your array is sparse, use a map (TreeMap or HashMap) of Long or BigInteger:
Map<Long,Long> t = new TreeMap<Long,Long>()
The memory consumption of sparse arrays depends on the number of elements actually stored, so you may want to delete values from the map that are no longer needed.
If your array is not sparse, use a 2-level array (memory consumption will depend on the pre-allocated size only):
public class LongArray {
static final long BLOCK_SIZE = 0x40000000;
long[][] storage;
public LongArray(long size) {
long blockCount = (size + BLOCK_SIZE - 1) / BLOCK_SIZE;
storage = new long[][(int) blockCount];
for (long i = 0; i < blockCount; i++) {
if (i == blockCount - 1) {
storage[i] = new long[(int) size - BLOCK_SIZE * (blockCount - 1)];
} else {
storage[i] = new long[(int) BLOCK_SIZE];
}
}
}
public long get(long index) {
return storage[(int) (index / BLOCK_SIZE)][(int) (index % BLOCK_SIZE)];
}
public void put(long index, long value) {
storage[(int) (index / BLOCK_SIZE)][(int) (index % BLOCK_SIZE)] = value;
}
}
In both cases, use t.get(index) and t.put(index, value) instead of t[index] to access your array (if t is the name of the array).
You can do one thing. Check if the value of n is equal to 1855656959555656 in the beginning or if its multiple. Suppose, the value of n is twice of 1855656959555656. Then you can create two arrays and link them together virtually. This should solve your problem but it will involve a lot of overhead.
Use recursive call:
int T(int n){
if (n==1 || n==4){
return 1;
} else if (n==2 || n==3){
return 3;
} else {
return T(n-1)+3*T(n-2)+3T*(n-3)+T(n-4);
}
}
Edit: Time consumming. Won't work with large numbers