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Explanation of Dynamic Programming solution

This is the problem: given a number of bricks n, between 3 and 200, return the number of different staircases that can be built. Each type of staircase should consist of 2 or more steps. No two steps are allowed to be at the same height - each step must be lower than the previous one. All steps must contain at least one brick. A step's height is classified as the total amount of bricks that make up that step.
For example, when N = 3, you have only 1 choice of how to build the staircase, with the first step having a height of 2 and the second step having a height of 1: (# indicates a brick)
#
##
21
When N = 4, you still only have 1 staircase choice:
#
#
##
31
But when N = 5, there are two ways you can build a staircase from the given bricks. The two staircases can have heights (4, 1) or (3, 2), as shown below:
#
#
#
##
41
#
##
##
32
I found a solution online, but I don't quite intuitively understand the dynamic programming solution.
public class Answer {
static int[][] p = new int[201][201];
public static void fillP() {
p[1][1] = 1;
p[2][2] = 1;
for (int w = 3; w < 201 ; w++) {
for (int m = 1; m <= w; m++) {
if (w-m == 0) {
p[w][m] = 1 + p[w][m-1];
} else if (w-m < m) {
p[w][m] = p[w-m][w-m] + p[w][m-1];
} else if (w-m == m) {
p[w][m] = p[m][m-1] + p[w][m-1];
} else if (w-m >m) {
p[w][m] = p[w-m][m-1] + p[w][m-1];
}
}
}
}
public static int answer(int n) {
fillP();
return p[n][n] - 1;
}
}
In particular, how would one come up with the relationships between each successive entry in the array?

This is a very interesting question. First, let's try to understand the recurrence relation:
If we currently built a step of height h and we have b bricks left to use, the number of ways we could complete the staircase from here is equal to the sum of all the ways we can complete the staircase with the next step of height h' and b - h' bricks, for 0 < h' < h.
Once we have that recurrence relation, we can devise a recursive solution; however, at it's current state, the solution runs in exponential time. So, we just need to "cache" our results:
import java.util.Scanner;
public class Stairs {
static int LIMIT = 200;
static int DIRTY = -1;
static int[][] cache = new int[LIMIT + 2][LIMIT + 2];
public static void clearCache() {
for (int i = 0; i <= LIMIT + 1; i++) {
for (int j = 0; j <= LIMIT + 1; j++) {
// mark cache as dirty/garbage values
cache[i][j] = DIRTY;
}
}
}
public static int numberOfStaircases(int level, int bricks, int steps) {
// base cases
if (bricks < 0) return 0;
if (bricks == 0 && steps >= 2) return 1;
// only compute answer if we haven't already
if (cache[level][bricks] == DIRTY) {
int ways = 0;
for (int nextLevel = level - 1; nextLevel > 0; nextLevel--) {
ways += numberOfStaircases(nextLevel, bricks - nextLevel, steps + 1);
}
cache[level][bricks] = ways;
}
return cache[level][bricks];
}
public static int answer(int n) {
clearCache();
return numberOfStaircases(n + 1, n, 0);
}
public static void main(String[] args) {
Scanner scanner = new Scanner(System.in);
int n = scanner.nextInt();
System.out.println(answer(n));
}
}
From the code you provided, it seems as if the author went one more step further and replaced the recursive solution with a purely iterative version. This means that the author made a bottom-up solution rather than a top-down solution.
We could also approach the problem more mathematically:
How many distinct non-trivial integer partitions does n have?
So for n = 6, we have: 5 + 1, 4 + 2, 3 + 2 + 1. So answer(6) = 3. Interestingly enough, Euler proved that the number of distinct integer partitions for a given n is always the same as the number of not necessarily distinct odd integer partitions.
(As a side note, I know where this question comes from. Good luck!)

Good explanation of this problem (The Grandest Staircase Of Them All) is on the page with several different solutions.
https://jtp.io/2016/07/26/dynamic-programming-python.html

For building a staircase, we can consider it as a pyramid to build on top of each step with the amount of bricks that remain with us as we ascend and complete our staircase.
For n bricks we have, we can start with i bricks on top of the first step, which means we have n-i bricks remaining with us for the current step. As we calculate the number of ways for building a multilevel staircase of n bricks, for first step n-i, the number of ways are - to build the staircase with i bricks which can either be multilevel or a single step. We can follow this relative mechanism to get the total number of staircases that are possible from the zeroth step with n bricks.
To avoid calculating the same results for a pyramid of bricks i, we can use an in memory cache which stores results of the possible staircases for n bricks with k as its last step (since the possible staircases will depend on the previous step over which the pyramid will be placed just to avoid double steps or last step becoming smaller than the next one).
package com.dp;
import java.util.HashMap;
import java.util.Map;
public class Staircases {
private static Map<String, Long> cacheNumberStaircasesForNBricks = new HashMap<String, Long>();
public static void main(String[] args) {
int bricks = 1000;
Long start = System.currentTimeMillis();
long numberOfStaircases = getStaircases(bricks, Integer.MAX_VALUE, true);
Long end = System.currentTimeMillis();
System.out.println(numberOfStaircases);
System.out.println("Time taken " + (end - start) + " ms");
}
/*
* For n bricks returns number of staircases can be formed with minimum 2
* stairs and no double steps, with k as the number of bricks in last step
*/
private static long getStaircases(int n, int k, boolean multilevelOnly) {
/*
* if the last step was same as n, you can't get a single step of n bricks as the next step,
* hence the staircase needs to be multilevel
*/
if (n == k) {
multilevelOnly = true;
}
/*
* for n less than 3 ie 1 or 2 there is only one stair case possible if the last step is of greater number of bricks
*/
if (n < 3) {
if (k <= n) {
return 0;
}
return 1;
}
/*
* for n =3, if multilevel is allowed only, then only one combination is
* there ie 2,1.
*/
if (n == 3) {
if (k < n) {
return 0;
}
if (multilevelOnly) {
return 1;
}
}
/*
* refer from the in-memory cache. Don't compute if we have computed for last step (k) and current bricks left (n) to build the rest of the staircase
*/
String cacheKey = n + "-" + k;
if (cacheNumberStaircasesForNBricks.get(cacheKey) != null) {
return cacheNumberStaircasesForNBricks.get(cacheKey);
}
/*
* start with one case which involves a single step of n bricks.
* for multilevel only or last step being smaller(invalid scenario) staircases, put the initial count as zero
*/
long numberOfStaircases = multilevelOnly || k < n ? 0 : 1;
for (int i = 1; n - i > 0; i++) {
// current step must be smaller than the last step
if (n - i < k) {
numberOfStaircases += getStaircases(i, n - i, false);
}
}
cacheNumberStaircasesForNBricks.put(cacheKey, numberOfStaircases);
return numberOfStaircases;
}
}

Restaurant Maximum Profit using Dynamic Programming

Its an assignment task,I have spend 2 days to come up with a solution but still having lots of confusion,however here I need to make few points clear. Following is the problem:
Yuckdonald’s is considering opening a series of restaurant along QVH. n possible locations are along a straight line and the distances of these locations from the start of QVH are in miles and in increasing order m1, m2, ...., mn. The constraints are as follows:
1. At each location, Yuckdonald may open one restaurant and expected profit from opening a restaurant at location i is given as pi
2. Any two restaurants should be at least k miles apart, where k is a positive integer
My solution:
public class RestaurantProblem {
int[] Profit;
int[] P;
int[] L;
int k;
public RestaurantProblem(int[] L , int[] P, int k) {
this.L = L;
this.P = P;
this.k = k;
Profit = new int[L.length];
}
public int compute(int i){
if(i==0)
return 0;
Profit[i]= P[i]+(L[i]-L[i-1]< k ? 0:compute(i-1));//if condition satisfies then adding previous otherwise zero
if (Profit[i]<compute(i-1)){
Profit[i] = compute(i-1);
}
return Profit[i];
}
public static void main(String args[]){
int[] m = {0,5,10,15,19,25,28,29};
int[] p = {0,10,4,61,21,13,19,15};
int k = 5;
RestaurantProblem rp = new RestaurantProblem(m, p ,k);
rp.compute(m.length-1);
for(int n : rp.Profit)
System.out.println(n);
}
}
This solution giving me 88 however if I exclude (Restaurant at 25 with Profit 13) and include (Restaurant 28 with profit 19) I can have 94 max...
point me if I am wrong or how can I achieve this if its true.

I was able to identify 2 mistakes:
You are not actually using dynamic programming
, you are just storing the results in a data structure, which wouldn't be that bad for performance if the program worked the way you have written it and if you did only 1 recursive call.
However you do at least 2 recursive calls. Therefore the program runs in Ω(2^n) instead of O(n).
Dynamic programming usually works like this (pseudocode):
calculate(input) {
if (value already calculated for input)
return previously calculated value
else
calculate and store value for input and return result
}
You could do this by initializing the array elements to -1 (or 0 if all profits are positive):
Profit = new int[L.length];
Arrays.fill(Profit, -1); // no need to do this, if you are using 0
public int compute(int i) {
if (Profit[i] >= 0) { // modify the check, if you're using 0 for non-calculated values
// reuse already calculated value
return Profit[i];
}
...
You assume the previous restaurant can only be build at the previous position
Profit[i] = P[i] + (L[i]-L[i-1]< k ? 0 : compute(i-1));
^
Just ignores all positions before i-1
Instead you should use the profit for the last position that is at least k miles away.
Example
k = 3
L 1 2 3 ... 100
P 5 5 5 ... 5
here L[i] - L[i-1] < k is true for all i and therefore the result will just be P[99] = 5 but it should be 34 * 5 = 170.
int[] lastPos;
public RestaurantProblem(int[] L, int[] P, int k) {
this.L = L;
this.P = P;
this.k = k;
Profit = new int[L.length];
lastPos = new int[L.length];
Arrays.fill(lastPos, -2);
Arrays.fill(Profit, -1);
}
public int computeLastPos(int i) {
if (i < 0) {
return -1;
}
if (lastPos[i] >= -1) {
return lastPos[i];
}
int max = L[i] - k;
int lastLastPos = computeLastPos(i - 1), temp;
while ((temp = lastLastPos + 1) < i && L[temp] <= max) {
lastLastPos++;
}
return lastPos[i] = lastLastPos;
}
public int compute(int i) {
if (i < 0) {
// no restaurants can be build before pos 0
return 0;
}
if (Profit[i] >= 0) { // modify the check, if you're using 0 for non-calculated values
// reuse already calculated value
return Profit[i];
}
int profitNoRestaurant = compute(i - 1);
if (P[i] <= 0) {
// no profit can be gained by building this restaurant
return Profit[i] = profitNoRestaurant;
}
return Profit[i] = Math.max(profitNoRestaurant, P[i] + compute(computeLastPos(i)));
}

To my understanding, the prolem can be modelled with a two-dimensional state space, which I don't find in the presented implementation. For each (i,j) in{0,...,n-1}times{0,...,n-1}` let
profit(i,j) := the maximum profit attainable for selecting locations
from {0,...,i} where the farthest location selected is
no further than at position j
(or minus infinity if no such solution exist)
and note that the recurrence relation
profit(i,j) = min{ p[i] + profit(i-1,lastpos(i)),
profit(i-1,j)
}
where lastpos(i) is the location which is farthest from the start, but no closer than k to position i; the first case above corresponds to selection location i into the solution while the second case corresponds to omitting location j in the solution. The overall solution can be obtained by evaluating profit(n-1,n-1); the evaluation can be done either recursively or by filling a two-dimensional array in a bottom-up manner and returning its contents at (n-1,n-1).

Adjacency matrix DFS traversal to find number of paths from x to y in DiGraph (Java)

private Stack<Integer> stack = new Stack<Integer>();
public int dfs(int maxDepth){ //path A-C with 'x' stops maximum
int src = 0;
int dest = 2;
int i;
int countDepth = 0;
int countPaths = 0;
int element;
stack.add(src);
while(!stack.isEmpty() && countDepth <= maxDepth)
{
element = stack.pop();
i = 0;
while(i < gSize) // i < 5
{
if(arr[element][i] > 0)
{
stack.add(i);
if(i == dest)
countPaths++;
}
i++;
}
countDepth++;
}
return countPaths;
}
The idea of this code is to find how many paths there are from point A to point B (arbitrary point A and B) with a maximum of 'x' amount of stops. So from C to C with a maximum of 3 stops, there are two possibilities:
C -> D -> C (2 stops)
C -> D - > E -> C (3 stops)
From A to C with a maximum of 3 stops there are 3 possibilities:
A -> B -> C (2 stops)
A -> D -> C (2 stops)
A -> E -> B -> C (3 stops)
However, it just finds one and the program stops. It is because of my countDepth variable. It stops when depth > maxDepth. In other words, it is not traversing my graph like I want it to, it goes down one branch then program stops. How do I keep track of the depth that it is currently at properly? Thank you!

You are increasing your depth every time you pop something off the stack, and notice you are never decreasing it. So you for loop will only execute gSize times.
During a depth first search though, once you reach the end of a path (max length or dead end), you need to go back up the tree, and your depth needs to decrease.
The way I would suggest approaching this is to store both the node and the depth in the stack.
So first you push (src, 0) onto the stack.
Then you pop say (node, i) off the stack, if i < gSize then push (child, i + 1) onto the stack for each child of node. And if node == dest, add one to your count.

And actually this can be solved a lot simpler and more efficient this way:
int count = -1;
Map<Integer , Integer> nodes = new HashMap<>();
nodes.put(src , 1);
//count the depth at which the algorithm currently is
for(int i = 0 ; i < maxDepth ; i++){
Map<Integer , Integer> next_nodes = new HashMap<>();
nodes.stream.forEach(e -> {
if(e.getKey() == dest)
count += e.getValue();
for(int j = 0 ; j < arr[e.getKey()].length ; j++)
if(arr[e.getKey()][j] > 0)
if(next_nodes.containsKey(j))
next_nodes.put(j , next_nodes.get(j) + e.getValue());
else
next_nodes.put(j , e.getValue());
});
nodes = next_nodes;
}
The basic idea behind this is to count the number of times we can end up at each node after n steps. This reduces the number of steps required per depth to a maximum of numberOfNodes, which is quite a lot better than the growth of number of steps in your code (~exponential).

How to write Extended Euclidean Algorithm code wise in Java?

I have a question which is actually requires a bit of understanding Euclidian Algorithm. Problem is simple. An int "First" and int "Second" numbers are given by the user via Scanner.
Than we need to find greatest common divisor of them. Than the process goes like explained below:
Now Assume that the First number is: 42 and the Second is: 30 - they've given by the user. -
int x, y;
(x * First) + (y * Second) = gcd(First, Second); // x ? y ?
To Find GCD you may use: gcd(First, Second); Code is below:
public static int gcd(int a, int b)
{
if(a == 0 || b == 0) return a+b; // base case
return gcd(b,a%b);
}
Sample Input: First: 24 Second: 48 and Output should be x: (-3) and y: 2
Sample Input: First: 42 Second: 30 and Output should be x: (-2) and y: 3
Sample Input: First: 35 Second: 05 and Output should be x: (0) and y: 1
(x * First) + (y * Second) = gcd(First, Second); // How can we find x and y ?
I would very appreciate it if you could show a solution code wise in java thanks for checking!

The Extended Euclidean Algorithm is described in this Wikipedia article. The basic algorithm is stated like this (it looks better in the Wikipedia article):
More precisely, the standard Euclidean algorithm with a and b as
input, consists of computing a sequence q1,...,
qk of quotients and a sequence r0,...,
rk+1 of remainders such that
r0=a r1=b ...
ri+1=ri-1-qi ri and 0 <
ri+1 < |ri| ...
It is the main property of Euclidean division that the inequalities on
the right define uniquely ri+1 from ri-1 and
ri.
The computation stops when one reaches a remainder rk+1
which is zero; the greatest common divisor is then the last non zero
remainder rk.
The extended Euclidean algorithm proceeds similarly, but adds two
other sequences defined by
s0=1, s1=0 t0=0,
t1=1 ...
si+1=si-1-qi si
ti+1=ti-1-qi ti
This should be easy to implement in Java, but the mathematical way it's expressed may make it hard to understand. I'll try to break it down.
Note that this is probably going to be easier to implement in a loop than recursively.
In the standard Euclidean algorithm, you compute ri+1 in terms of ri-1 and ri. This means that you have to save the two previous versions of r. This part of the formula:
ri+1=ri-1-qi ri and 0 <
ri+1 < |ri| ...
just means that ri+1 will be the remainder when ri-1 is divided by ri. qi is the quotient, which you don't use in the standard Euclidean algorithm, but you do use in the extended one. So Java code to perform the standard Euclidean algorithm (i.e. compute the GCD) might look like:
prevPrevR = a;
prevR = b;
while ([something]) {
nextR = prevPrevR % prevR;
quotient = prevPrevR / prevR; // not used in the standard algorithm
prevPrevR = prevR;
prevR = nextR;
}
Thus, at any point, prevPrevR will be essentially ri-1, and prevR will be ri. The algorithm computes the next r, ri+1, then shifts everything which in essence increments i by 1.
The extended Euclidean algorithm will be done the same way, saving two s values prevPrevS and prevS, and two t values prevPrevT and prevT. I'll let you work out the details.

Thank's for helping me out ajb I solved it after digging your answer. So for the people who would like to see code wise:
public class Main
{
public static void main (String args[])
{
#SuppressWarnings("resource")
System.out.println("How many times you would like to try ?")
Scanner read = new Scanner(System.in);
int len = read.nextInt();
for(int w = 0; w < len; w++)
{
System.out.print("Please give the numbers seperated by space: ")
read.nextLine();
long tmp = read.nextLong();
long m = read.nextLong();
long n;
if (m < tmp) {
n = m;
m = tmp;
}
else {
n = tmp;
}
long[] l1 = {m, 1, 0};
long[] l2 = {n, 0, 1};
long[] l3 = new long[3];
while (l1[0]-l2[0]*(l1[0]/l2[0]) > 0) {
for (int j=0;j<3;j++) l3[j] = l2[j];
long q = l1[0]/l2[0];
for (int i = 0; i < 3; i++) {
l2[i] = (l1[i]-l2[i]*q);
}
for (int k=0;k<3;k++) l1[k] = l3[k];
}
System.out.printf("%d %d %d",l2[1],l2[2],l2[0]); // first two Bezouts identity Last One gcd
}
}
}

Here is the code that I came up with if anyone is still looking. It is in C# but I am sure it similar to java. Enjoy
static void Main(string[] args)
{
List<long> U = new List<long>();
List<long> V = new List<long>();
List<long> W = new List<long>();
long a, b, d, x, y;
Console.Write("Enter value for a: ");
string firstInput = Console.ReadLine();
long.TryParse(firstInput, out a);
Console.Write("Enter value for b: ");
string secondInput = Console.ReadLine();
long.TryParse(secondInput, out b);
long temp;
//Make sure that a > b
if(a < b)
{
temp = a;
a = b;
b = temp;
}
//Initialise List U
U.Add(a);
U.Add(1);
U.Add(0);
//Initialise List V
V.Add(b);
V.Add(0);
V.Add(1);
while(V[0] > 0)
{
decimal difference = U[0] / V[0];
var roundedDown = Math.Floor(difference);
long rounded = Convert.ToInt64(roundedDown);
for (int i = 0; i < 3; i++)
W.Add(U[i] - rounded * V[i]);
U.Clear();
for (int i = 0; i < 3; i++)
U.Add(V[i]);
V.Clear();
for (int i = 0; i < 3; i++)
V.Add(W[i]);
W.Clear();
}
d = U[0];
x = U[1];
y = U[2];
Console.WriteLine("\nd = {0}, x = {1}, y = {2}", d, x, y);
//Check Equation
Console.WriteLine("\nEquation check: d = ax + by\n");
Console.WriteLine("\t{0} = {1}({2}) + {3}({4})", d, a, x, b, y);
Console.WriteLine("\t{0} = {1} + {2}", d, a*x, b*y);
Console.WriteLine("\t{0} = {1}", d, (a * x) + (b * y));
if (d == (a * x) + (b * y))
Console.WriteLine("\t***Equation is satisfied!***");
else
Console.WriteLine("\tEquation is NOT satisfied!");
}
}
}

Modifying Levenshtein Distance algorithm to not calculate all distances

I'm working on a fuzzy search implementation and as part of the implementation, we're using Apache's StringUtils.getLevenshteinDistance. At the moment, we're going for a specific maxmimum average response time for our fuzzy search. After various enhancements and with some profiling, the place where the most time is spent is calculating the Levenshtein distance. It takes up roughly 80-90% of the total time on search strings three letters or more.
Now, I know there are some limitations to what can be done here, but I've read on previous SO questions and on the Wikipedia link for LD that if one is willing limit the threshold to a set maximum distance, that could help curb the time spent on the algorithm, but I'm not sure how to do this exactly.
If we are only interested in the
distance if it is smaller than a
threshold k, then it suffices to
compute a diagonal stripe of width
2k+1 in the matrix. In this way, the
algorithm can be run in O(kl) time,
where l is the length of the shortest
string.[3]
Below you will see the original LH code from StringUtils. After that is my modification. I'm trying to basically calculate the distances of a set length from the i,j diagonal (so, in my example, two diagonals above and below the i,j diagonal). However, this can't be correct as I've done it. For example, on the highest diagonal, it's always going to choose the cell value directly above, which will be 0. If anyone could show me how to make this functional as I've described, or some general advice on how to make it so, it would be greatly appreciated.
public static int getLevenshteinDistance(String s, String t) {
if (s == null || t == null) {
throw new IllegalArgumentException("Strings must not be null");
}
int n = s.length(); // length of s
int m = t.length(); // length of t
if (n == 0) {
return m;
} else if (m == 0) {
return n;
}
if (n > m) {
// swap the input strings to consume less memory
String tmp = s;
s = t;
t = tmp;
n = m;
m = t.length();
}
int p[] = new int[n+1]; //'previous' cost array, horizontally
int d[] = new int[n+1]; // cost array, horizontally
int _d[]; //placeholder to assist in swapping p and d
// indexes into strings s and t
int i; // iterates through s
int j; // iterates through t
char t_j; // jth character of t
int cost; // cost
for (i = 0; i<=n; i++) {
p[i] = i;
}
for (j = 1; j<=m; j++) {
t_j = t.charAt(j-1);
d[0] = j;
for (i=1; i<=n; i++) {
cost = s.charAt(i-1)==t_j ? 0 : 1;
// minimum of cell to the left+1, to the top+1, diagonally left and up +cost
d[i] = Math.min(Math.min(d[i-1]+1, p[i]+1), p[i-1]+cost);
}
// copy current distance counts to 'previous row' distance counts
_d = p;
p = d;
d = _d;
}
// our last action in the above loop was to switch d and p, so p now
// actually has the most recent cost counts
return p[n];
}
My modifications (only to the for loops):
for (j = 1; j<=m; j++) {
t_j = t.charAt(j-1);
d[0] = j;
int k = Math.max(j-2, 1);
for (i = k; i <= Math.min(j+2, n); i++) {
cost = s.charAt(i-1)==t_j ? 0 : 1;
// minimum of cell to the left+1, to the top+1, diagonally left and up +cost
d[i] = Math.min(Math.min(d[i-1]+1, p[i]+1), p[i-1]+cost);
}
// copy current distance counts to 'previous row' distance counts
_d = p;
p = d;
d = _d;
}

The issue with implementing the window is dealing with the value to the left of the first entry and above the last entry in each row.
One way is to start the values you initially fill in at 1 instead of 0, then just ignore any 0s that you encounter. You'll have to subtract 1 from your final answer.
Another way is to fill the entries left of first and above last with high values so the minimum check will never pick them. That's the way I chose when I had to implement it the other day:
public static int levenshtein(String s, String t, int threshold) {
int slen = s.length();
int tlen = t.length();
// swap so the smaller string is t; this reduces the memory usage
// of our buffers
if(tlen > slen) {
String stmp = s;
s = t;
t = stmp;
int itmp = slen;
slen = tlen;
tlen = itmp;
}
// p is the previous and d is the current distance array; dtmp is used in swaps
int[] p = new int[tlen + 1];
int[] d = new int[tlen + 1];
int[] dtmp;
// the values necessary for our threshold are written; the ones after
// must be filled with large integers since the tailing member of the threshold
// window in the bottom array will run min across them
int n = 0;
for(; n < Math.min(p.length, threshold + 1); ++n)
p[n] = n;
Arrays.fill(p, n, p.length, Integer.MAX_VALUE);
Arrays.fill(d, Integer.MAX_VALUE);
// this is the core of the Levenshtein edit distance algorithm
// instead of actually building the matrix, two arrays are swapped back and forth
// the threshold limits the amount of entries that need to be computed if we're
// looking for a match within a set distance
for(int row = 1; row < s.length()+1; ++row) {
char schar = s.charAt(row-1);
d[0] = row;
// set up our threshold window
int min = Math.max(1, row - threshold);
int max = Math.min(d.length, row + threshold + 1);
// since we're reusing arrays, we need to be sure to wipe the value left of the
// starting index; we don't have to worry about the value above the ending index
// as the arrays were initially filled with large integers and we progress to the right
if(min > 1)
d[min-1] = Integer.MAX_VALUE;
for(int col = min; col < max; ++col) {
if(schar == t.charAt(col-1))
d[col] = p[col-1];
else
// min of: diagonal, left, up
d[col] = Math.min(p[col-1], Math.min(d[col-1], p[col])) + 1;
}
// swap our arrays
dtmp = p;
p = d;
d = dtmp;
}
if(p[tlen] == Integer.MAX_VALUE)
return -1;
return p[tlen];
}

I've written about Levenshtein automata, which are one way to do this sort of check in O(n) time before, here. The source code samples are in Python, but the explanations should be helpful, and the referenced papers provide more details.

According to "Gusfield, Dan (1997). Algorithms on strings, trees, and sequences: computer science and computational biology" (page 264) you should ignore zeros.

Here someone answers a very similar question:
Cite:
I've done it a number of times. The way I do it is with a recursive depth-first tree-walk of the game tree of possible changes. There is a budget k of changes, that I use to prune the tree. With that routine in hand, first I run it with k=0, then k=1, then k=2 until I either get a hit or I don't want to go any higher.
char* a = /* string 1 */;
char* b = /* string 2 */;
int na = strlen(a);
int nb = strlen(b);
bool walk(int ia, int ib, int k){
/* if the budget is exhausted, prune the search */
if (k < 0) return false;
/* if at end of both strings we have a match */
if (ia == na && ib == nb) return true;
/* if the first characters match, continue walking with no reduction in budget */
if (ia < na && ib < nb && a[ia] == b[ib] && walk(ia+1, ib+1, k)) return true;
/* if the first characters don't match, assume there is a 1-character replacement */
if (ia < na && ib < nb && a[ia] != b[ib] && walk(ia+1, ib+1, k-1)) return true;
/* try assuming there is an extra character in a */
if (ia < na && walk(ia+1, ib, k-1)) return true;
/* try assuming there is an extra character in b */
if (ib < nb && walk(ia, ib+1, k-1)) return true;
/* if none of those worked, I give up */
return false;
}
just the main part, more code in the original

I used the original code and places this just before the end of the j for loop:
if (p[n] > s.length() + 5)
break;
The +5 is arbitrary but for our purposes, if the distances is the query length plus five (or whatever number we settle upon), it doesn't really matter what is returned because we consider the match as simply being too different. It does cut down on things a bit. Still, pretty sure this isn't the idea that the Wiki statement was talking about, if anyone understands that better.

Apache Commons Lang 3.4 has this implementation:
/**
* <p>Find the Levenshtein distance between two Strings if it's less than or equal to a given
* threshold.</p>
*
* <p>This is the number of changes needed to change one String into
* another, where each change is a single character modification (deletion,
* insertion or substitution).</p>
*
* <p>This implementation follows from Algorithms on Strings, Trees and Sequences by Dan Gusfield
* and Chas Emerick's implementation of the Levenshtein distance algorithm from
* http://www.merriampark.com/ld.htm</p>
*
* <pre>
* StringUtils.getLevenshteinDistance(null, *, *) = IllegalArgumentException
* StringUtils.getLevenshteinDistance(*, null, *) = IllegalArgumentException
* StringUtils.getLevenshteinDistance(*, *, -1) = IllegalArgumentException
* StringUtils.getLevenshteinDistance("","", 0) = 0
* StringUtils.getLevenshteinDistance("aaapppp", "", 8) = 7
* StringUtils.getLevenshteinDistance("aaapppp", "", 7) = 7
* StringUtils.getLevenshteinDistance("aaapppp", "", 6)) = -1
* StringUtils.getLevenshteinDistance("elephant", "hippo", 7) = 7
* StringUtils.getLevenshteinDistance("elephant", "hippo", 6) = -1
* StringUtils.getLevenshteinDistance("hippo", "elephant", 7) = 7
* StringUtils.getLevenshteinDistance("hippo", "elephant", 6) = -1
* </pre>
*
* #param s the first String, must not be null
* #param t the second String, must not be null
* #param threshold the target threshold, must not be negative
* #return result distance, or {#code -1} if the distance would be greater than the threshold
* #throws IllegalArgumentException if either String input {#code null} or negative threshold
*/
public static int getLevenshteinDistance(CharSequence s, CharSequence t, final int threshold) {
if (s == null || t == null) {
throw new IllegalArgumentException("Strings must not be null");
}
if (threshold < 0) {
throw new IllegalArgumentException("Threshold must not be negative");
}
/*
This implementation only computes the distance if it's less than or equal to the
threshold value, returning -1 if it's greater. The advantage is performance: unbounded
distance is O(nm), but a bound of k allows us to reduce it to O(km) time by only
computing a diagonal stripe of width 2k + 1 of the cost table.
It is also possible to use this to compute the unbounded Levenshtein distance by starting
the threshold at 1 and doubling each time until the distance is found; this is O(dm), where
d is the distance.
One subtlety comes from needing to ignore entries on the border of our stripe
eg.
p[] = |#|#|#|*
d[] = *|#|#|#|
We must ignore the entry to the left of the leftmost member
We must ignore the entry above the rightmost member
Another subtlety comes from our stripe running off the matrix if the strings aren't
of the same size. Since string s is always swapped to be the shorter of the two,
the stripe will always run off to the upper right instead of the lower left of the matrix.
As a concrete example, suppose s is of length 5, t is of length 7, and our threshold is 1.
In this case we're going to walk a stripe of length 3. The matrix would look like so:
1 2 3 4 5
1 |#|#| | | |
2 |#|#|#| | |
3 | |#|#|#| |
4 | | |#|#|#|
5 | | | |#|#|
6 | | | | |#|
7 | | | | | |
Note how the stripe leads off the table as there is no possible way to turn a string of length 5
into one of length 7 in edit distance of 1.
Additionally, this implementation decreases memory usage by using two
single-dimensional arrays and swapping them back and forth instead of allocating
an entire n by m matrix. This requires a few minor changes, such as immediately returning
when it's detected that the stripe has run off the matrix and initially filling the arrays with
large values so that entries we don't compute are ignored.
See Algorithms on Strings, Trees and Sequences by Dan Gusfield for some discussion.
*/
int n = s.length(); // length of s
int m = t.length(); // length of t
// if one string is empty, the edit distance is necessarily the length of the other
if (n == 0) {
return m <= threshold ? m : -1;
} else if (m == 0) {
return n <= threshold ? n : -1;
}
if (n > m) {
// swap the two strings to consume less memory
final CharSequence tmp = s;
s = t;
t = tmp;
n = m;
m = t.length();
}
int p[] = new int[n + 1]; // 'previous' cost array, horizontally
int d[] = new int[n + 1]; // cost array, horizontally
int _d[]; // placeholder to assist in swapping p and d
// fill in starting table values
final int boundary = Math.min(n, threshold) + 1;
for (int i = 0; i < boundary; i++) {
p[i] = i;
}
// these fills ensure that the value above the rightmost entry of our
// stripe will be ignored in following loop iterations
Arrays.fill(p, boundary, p.length, Integer.MAX_VALUE);
Arrays.fill(d, Integer.MAX_VALUE);
// iterates through t
for (int j = 1; j <= m; j++) {
final char t_j = t.charAt(j - 1); // jth character of t
d[0] = j;
// compute stripe indices, constrain to array size
final int min = Math.max(1, j - threshold);
final int max = (j > Integer.MAX_VALUE - threshold) ? n : Math.min(n, j + threshold);
// the stripe may lead off of the table if s and t are of different sizes
if (min > max) {
return -1;
}
// ignore entry left of leftmost
if (min > 1) {
d[min - 1] = Integer.MAX_VALUE;
}
// iterates through [min, max] in s
for (int i = min; i <= max; i++) {
if (s.charAt(i - 1) == t_j) {
// diagonally left and up
d[i] = p[i - 1];
} else {
// 1 + minimum of cell to the left, to the top, diagonally left and up
d[i] = 1 + Math.min(Math.min(d[i - 1], p[i]), p[i - 1]);
}
}
// copy current distance counts to 'previous row' distance counts
_d = p;
p = d;
d = _d;
}
// if p[n] is greater than the threshold, there's no guarantee on it being the correct
// distance
if (p[n] <= threshold) {
return p[n];
}
return -1;
}