Find the Big-O and explain why: The following code prints all strings of length k where the characters are in sorted order. Itdoes this by generating all strings of length k and then checking if each is sorted.1 int numchars = 26;2 void printsortedstrings (int remaining) {printsortedstrings(remaining, "");3456 void printsortedstrings (int remaining, String prefix) {if (remaining-- 0) {if (isinorder(prefix)) {System.out.println(prefix);71011else (for (int i- e; i < numchars; i++) {char c= ithletter(i);printsortedstrings (remaining - 1, prefix + c);12131415161718 }1920 boolean isinorder(String s) {for (int i= 1; i< s.length(); i++) {int prev ithletter(s.charAt(i - 1));int curr = ithLetter(s.charat(i));if (prev > curr) {return false;2122232425262728return true;29 }30char ithLetter(int i) {return (char) (((int) 'a') + i);313233 )|

Answered: Find the Big-O and explain why:

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

Find the Big-O and explain why:

The following code prints all strings of length k where the characters are in sorted order. It
does this by generating all strings of length k and then checking if each is sorted.
1 int numchars = 26;
2 void printsortedstrings (int remaining) {
printsortedstrings(remaining, "");
3
4
5
6 void printsortedstrings (int remaining, String prefix) {
if (remaining-- 0) {
if (isinorder(prefix)) {
System.out.println(prefix);
7
10
11
else (
for (int i- e; i < numchars; i++) {
char c= ithletter(i);
printsortedstrings (remaining - 1, prefix + c);
12
13
14
15
16
17
18 }
19
20 boolean isinorder(String s) {
for (int i= 1; i< s.length(); i++) {
int prev ithletter(s.charAt(i - 1));
int curr = ithLetter(s.charat(i));
if (prev > curr) {
return false;
21
22
23
24
25
26
27
28
return true;
29 }
30
char ithLetter(int i) {
return (char) (((int) 'a') + i);
31
32
33 )|

Expert Solution

Step 1

Time complexity is a measure of the amount of time an algorithm takes to solve a problem as a function of the size of the input to the problem.

It describes how the running time of an algorithm increases as the size of the input increases.

Time complexity is usually expressed using "big O" notation, which gives an upper bound on the growth rate of the running time as a function of the size of the input.

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