Minimum squares to cover a rectangle
Last Updated :
11 Jul, 2025
Given a rectangle with length l and breadth b, we need to find the minimum number of squares that can cover the surface of the rectangle, given that each square has a side of length a. It is allowed to cover the surface larger than the rectangle, but the rectangle has to be covered. It is not allowed to break the square.
Examples:
Input : 1 2 3
Output :1
We have a 3x3 square and we need
to make a rectangle of size 1x2.
So we need only 1 square to cover the
rectangle.
Input : 11 23 14
Output :2
The only way to actually fill the rectangle optimally is to arrange each square such that it is parallel to the sides of the rectangle. So we just need to find the number of squares to fully cover the length and breadth of the rectangle.
The length of the rectangle is l, and if the side length of the square is a divided l, then there must be l/a squares to cover the full length of l. If l isn't divisible by a, we need to add 1 to l/a, to round it down. For this, we can use the ceil function, as ceil(x) returns the least integer which is above or equal to x.
We can do the same with the rectangle width and take the number of squares across the width to be ceil(b/a).
So, total number of squares=ceil(m/a) * ceil(n/a).
C++
// C++ program to find the minimum number
// of squares to cover the surface of the
// rectangle with given dimensions
#include <bits/stdc++.h>
using namespace std;
int squares(int l, int b, int a)
{
// function to count
// the number of squares that can
// cover the surface of the rectangle
return ceil(l / (double)a) * ceil(b / (double)a);
}
// Driver code
int main()
{
int l = 11, b = 23, a = 14;
cout << squares(l, b, a) << endl;
return 0;
}
Java
// Java program to find the minimum number
// of squares to cover the surface of the
// rectangle with given dimensions
class GFG
{
static int squares(int l, int b, int a)
{
// function to count
// the number of squares that can
// cover the surface of the rectangle
return (int)(Math.ceil(l / (double)a) *
Math.ceil(b / (double)a));
}
// Driver code
public static void main(String[] args)
{
int l = 11, b = 23, a = 14;
System.out.println(squares(l, b, a));
}
}
// This code is contributed by ChitraNayal
Python
# Python3 program to find the minimum number
# of squares to cover the surface of the
# rectangle with given dimensions
import math
def squares(l, b, a):
# function to count
# the number of squares that can
# cover the surface of the rectangle
return math.ceil(l / a) * math.ceil(b / a)
# Driver code
if __name__ == "__main__":
l = 11
b = 23
a = 14
print(squares(l, b, a))
# This code is contributed
# by ChitraNayal
C#
// C# program to find the minimum number
// of squares to cover the surface of the
// rectangle with given dimensions
using System;
class GFG
{
static int squares(int l, int b, int a)
{
// function to count
// the number of squares that can
// cover the surface of the rectangle
return (int)(Math.Ceiling(l / (double)a) *
Math.Ceiling(b / (double)a));
}
// Driver code
public static void Main()
{
int l = 11, b = 23, a = 14;
Console.Write(squares(l, b, a));
}
}
// This code is contributed by ChitraNayal
JavaScript
<script>
// javascript program to find the minimum number
// of squares to cover the surface of the
// rectangle with given dimensions
function squares(l , b , a)
{
// function to count
// the number of squares that can
// cover the surface of the rectangle
return parseInt(Math.ceil(l / a) *
Math.ceil(b / a));
}
// Driver code
var l = 11, b = 23, a = 14;
document.write(squares(l, b, a));
// This code is contributed by Amit Katiyar
</script>
PHP
<?php
// PHP program to find the minimum number
// of squares to cover the surface of the
// rectangle with given dimensions
function squares($l, $b, $a)
{
// function to count
// the number of squares that can
// cover the surface of the rectangle
return ceil($l / (double)$a) *
ceil($b / (double)$a);
}
// Driver code
$l = 11;
$b = 23;
$a = 14;
echo squares($l, $b, $a);
// This code is contributed
// by ChitraNayal
?>
Time complexity: O(1), since there is no loop or recursion.
Auxiliary Space: O(1), since no extra space has been taken.
New Approach:- One alternative approach to solve this problem is to use the concept of the greatest common divisor (GCD) between the length and width of the rectangle.
Let's say the length of the rectangle is l and the width is b. We can find their GCD using the Euclidean algorithm, which states that:
GCD(l, b) = GCD(b, l % b), where "%" denotes the modulo operation.
We can keep applying this formula until b becomes 0, at which point the GCD will be equal to l.
Now, let's say that the GCD is equal to g. We can divide both the length and width by g, which will give us a new rectangle with dimensions (l/g) x (b/g).
The minimum number of squares needed to cover this new rectangle can be calculated as ceil((l/g) / a) * ceil((b/g) / a).
Finally, the minimum number of squares needed to cover the original rectangle is the product of the GCD and the number of squares needed for the new rectangle, i.e.,
minimum squares = g * ceil((l/g) / a) * ceil((b/g) / a)
Here's the implementation of this approach:
C++
// C++ program to find the minimum number
// of squares to cover the surface of the
// rectangle with given dimensions
#include <bits/stdc++.h>
using namespace std;
// function to find GCD using the Euclidean algorithm
int gcd(int a, int b)
{
if (b == 0)
return a;
return gcd(b, a % b);
}
// function to count the number of squares
// needed to cover a new rectangle with dimensions (l/g) x (b/g)
int countSquares(int l, int b, int a, int g)
{
return ceil((l / (double)g) / a) * ceil((b / (double)g) / a);
}
// Driver code
int main()
{
int l = 11, b = 23, a = 14;
int g = gcd(l, b);
int squares = g * countSquares(l, b, a, g);
cout << squares << endl;
return 0;
}
Java
import java.util.*;
public class Main {
// function to find GCD using the Euclidean algorithm
public static int gcd(int a, int b) {
if (b == 0)
return a;
return gcd(b, a % b);
}
// function to count the number of squares
// needed to cover a new rectangle with dimensions (l/g) x (b/g)
public static int countSquares(int l, int b, int a, int g) {
return (int) Math.ceil((l / (double) g) / a) * (int) Math.ceil((b / (double) g) / a);
}
// Driver code
public static void main(String[] args) {
int l = 11, b = 23, a = 14;
int g = gcd(l, b);
int squares = g * countSquares(l, b, a, g);
System.out.println(squares);
}
}
Python
import math
# function to find GCD using the Euclidean algorithm
def gcd(a, b):
if b == 0:
return a
return gcd(b, a % b)
# function to count the number of squares
# needed to cover a new rectangle with dimensions (l/g) x (b/g)
def count_squares(l, b, a, g):
return math.ceil((l / g) / a) * math.ceil((b / g) / a)
# Driver code
def main():
l = 11
b = 23
a = 14
g = gcd(l, b)
squares = g * count_squares(l, b, a, g)
print(squares)
if __name__ == "__main__":
main()
C#
using System;
class Program
{
// function to find GCD using the Euclidean algorithm
static int gcd(int a, int b)
{
if (b == 0)
return a;
return gcd(b, a % b);
}
// function to count the number of squares
// needed to cover a new rectangle with dimensions (l/g) x (b/g)
static int countSquares(int l, int b, int a, int g)
{
return (int)Math.Ceiling((l / (double)g) / a) * (int)Math.Ceiling((b / (double)g) / a);
}
// Driver code
static void Main(string[] args)
{
int l = 11, b = 23, a = 14;
int g = gcd(l, b);
int squares = g * countSquares(l, b, a, g);
Console.WriteLine(squares);
}
}
JavaScript
// Function to find GCD using the Euclidean algorithm
function gcd(a, b) {
if (b === 0) {
return a;
}
return gcd(b, a % b);
}
// Function to count the number of squares
// needed to cover a new rectangle with dimensions (l/g) x (b/g)
function countSquares(l, b, a, g) {
return Math.ceil((l / g) / a) * Math.ceil((b / g) / a);
}
// Driver code
function main() {
const l = 11;
const b = 23;
const a = 14;
const g = gcd(l, b);
const squares = g * countSquares(l, b, a, g);
console.log(squares);
}
main();
Output:-
2
Time complexity: -The time complexity of the given algorithm is O(log(min(l, b))), where l and b are the length and breadth of the rectangle, respectively. This is because the time complexity of the Euclidean algorithm to compute the GCD of two numbers a and b is O(log(min(a, b))), and we are using it to find the GCD of l and b. The time complexity of the countSquares function is O(1) because it performs constant time operations.
Auxiliary Space:- The auxiliary space complexity of the given algorithm is O(1), as it uses a constant amount of extra space to store the variables used in the algorithm.
Similar Reads
Basics & Prerequisites
Data Structures
Array Data StructureIn this article, we introduce array, implementation in different popular languages, its basic operations and commonly seen problems / interview questions. An array stores items (in case of C/C++ and Java Primitive Arrays) or their references (in case of Python, JS, Java Non-Primitive) at contiguous
3 min read
String in Data StructureA string is a sequence of characters. The following facts make string an interesting data structure.Small set of elements. Unlike normal array, strings typically have smaller set of items. For example, lowercase English alphabet has only 26 characters. ASCII has only 256 characters.Strings are immut
2 min read
Hashing in Data StructureHashing is a technique used in data structures that efficiently stores and retrieves data in a way that allows for quick access. Hashing involves mapping data to a specific index in a hash table (an array of items) using a hash function. It enables fast retrieval of information based on its key. The
2 min read
Linked List Data StructureA linked list is a fundamental data structure in computer science. It mainly allows efficient insertion and deletion operations compared to arrays. Like arrays, it is also used to implement other data structures like stack, queue and deque. Hereâs the comparison of Linked List vs Arrays Linked List:
2 min read
Stack Data StructureA Stack is a linear data structure that follows a particular order in which the operations are performed. The order may be LIFO(Last In First Out) or FILO(First In Last Out). LIFO implies that the element that is inserted last, comes out first and FILO implies that the element that is inserted first
2 min read
Queue Data StructureA Queue Data Structure is a fundamental concept in computer science used for storing and managing data in a specific order. It follows the principle of "First in, First out" (FIFO), where the first element added to the queue is the first one to be removed. It is used as a buffer in computer systems
2 min read
Tree Data StructureTree Data Structure is a non-linear data structure in which a collection of elements known as nodes are connected to each other via edges such that there exists exactly one path between any two nodes. Types of TreeBinary Tree : Every node has at most two childrenTernary Tree : Every node has at most
4 min read
Graph Data StructureGraph Data Structure is a collection of nodes connected by edges. It's used to represent relationships between different entities. If you are looking for topic-wise list of problems on different topics like DFS, BFS, Topological Sort, Shortest Path, etc., please refer to Graph Algorithms. Basics of
3 min read
Trie Data StructureThe Trie data structure is a tree-like structure used for storing a dynamic set of strings. It allows for efficient retrieval and storage of keys, making it highly effective in handling large datasets. Trie supports operations such as insertion, search, deletion of keys, and prefix searches. In this
15+ min read
Algorithms
Searching AlgorithmsSearching algorithms are essential tools in computer science used to locate specific items within a collection of data. In this tutorial, we are mainly going to focus upon searching in an array. When we search an item in an array, there are two most common algorithms used based on the type of input
2 min read
Sorting AlgorithmsA Sorting Algorithm is used to rearrange a given array or list of elements in an order. For example, a given array [10, 20, 5, 2] becomes [2, 5, 10, 20] after sorting in increasing order and becomes [20, 10, 5, 2] after sorting in decreasing order. There exist different sorting algorithms for differ
3 min read
Introduction to RecursionThe process in which a function calls itself directly or indirectly is called recursion and the corresponding function is called a recursive function. A recursive algorithm takes one step toward solution and then recursively call itself to further move. The algorithm stops once we reach the solution
14 min read
Greedy AlgorithmsGreedy algorithms are a class of algorithms that make locally optimal choices at each step with the hope of finding a global optimum solution. At every step of the algorithm, we make a choice that looks the best at the moment. To make the choice, we sometimes sort the array so that we can always get
3 min read
Graph AlgorithmsGraph is a non-linear data structure like tree data structure. The limitation of tree is, it can only represent hierarchical data. For situations where nodes or vertices are randomly connected with each other other, we use Graph. Example situations where we use graph data structure are, a social net
3 min read
Dynamic Programming or DPDynamic Programming is an algorithmic technique with the following properties.It is mainly an optimization over plain recursion. Wherever we see a recursive solution that has repeated calls for the same inputs, we can optimize it using Dynamic Programming. The idea is to simply store the results of
3 min read
Bitwise AlgorithmsBitwise algorithms in Data Structures and Algorithms (DSA) involve manipulating individual bits of binary representations of numbers to perform operations efficiently. These algorithms utilize bitwise operators like AND, OR, XOR, NOT, Left Shift, and Right Shift.BasicsIntroduction to Bitwise Algorit
4 min read
Advanced
Segment TreeSegment Tree is a data structure that allows efficient querying and updating of intervals or segments of an array. It is particularly useful for problems involving range queries, such as finding the sum, minimum, maximum, or any other operation over a specific range of elements in an array. The tree
3 min read
Pattern SearchingPattern searching algorithms are essential tools in computer science and data processing. These algorithms are designed to efficiently find a particular pattern within a larger set of data. Patten SearchingImportant Pattern Searching Algorithms:Naive String Matching : A Simple Algorithm that works i
2 min read
GeometryGeometry is a branch of mathematics that studies the properties, measurements, and relationships of points, lines, angles, surfaces, and solids. From basic lines and angles to complex structures, it helps us understand the world around us.Geometry for Students and BeginnersThis section covers key br
2 min read
Interview Preparation
Practice Problem