Policemen catch thieves Last Updated : 27 Mar, 2025 Comments Improve Suggest changes Like Article Like Report Try it on GfG Practice Given an array arr, where each element represents either a policeman (P) or a thief (T). The objective is to determine the maximum number of thieves that can be caught under the following conditions:Each policeman (P) can catch only one thief (T).A policeman can only catch a thief if the distance between them is at most k units.Your task is to find the maximum number of thieves that can be caught following these rules.Examples: Input: arr[] = ['P', 'T', 'T', 'P', 'T'], k = 1 Output: 2.Explanations: Here maximum 2 thieves can be caught, firstpoliceman catches first thief and second police man can catch either second or third thief.Input: arr[] = ['T', 'T', 'P'], k = 1Output: 1Explanations: Here second thief can be caught by the police.Table of Content[Naive Approach] Using nested loops - O(n*k) Time and O(n) Space[Expected Approach] Using two pointers - O(n) Time and O(1) Space[Naive Approach] Using Nested loops - O(n*k) Time and O(n) SpaceThe main idea is that for every policeman, we search for a thief within a range of 2*k + 1 in a sequential manner. If a thief is found, we insert their index into the caught array to ensure that the same thief is not caught multiple times.Step by Step implementation:We traverse the array from left to right.If the current element is a policeman (P), search for a thief (T) within a range of [i - k, i + k].Each thief can be caught only once, so we track caught thieves using an auxiliary array.If a thief is found within this range who is not already caught, mark them as caught and increase the count.Return the count of caught thieves. C++ // C++ program to find maximum number of thieves // caught #include <bits/stdc++.h> using namespace std; // Returns maximum number of thieves that can // be caught. int catchThieves(vector<char> &arr, int k) { int n = arr.size(); // To mark if a thief is caught vector<bool> caught(n, false); // Stores the number of caught thieves int count = 0; for (int i = 0; i < n; ++i) { if (arr[i] == 'P') { int start = max(0, i - k); int end = min(n - 1, i + k); // Search for an uncaught thief within range for (int j = start; j <= end; ++j) { if (arr[j] == 'T' && !caught[j]) { caught[j] = true; count++; break; } } } } return count; } int main() { int k = 1; vector<char> arr = { 'P', 'T', 'T', 'P', 'T' }; cout<< catchThieves(arr, k) << endl; } Java // Java program to find maximum number of thieves // caught import java.util.*; public class GfG { // Function to find the maximum number of thieves // that can be caught static int catchThieves(char[] arr, int k) { int n = arr.length; // Boolean array to keep track of caught thieves boolean[] caught = new boolean[n]; // Count of caught thieves int count = 0; // Traverse the array to find policemen for (int i = 0; i < n; i++) { // Found a policeman if (arr[i] == 'P') { int start = Math.max(0, i - k); int end = Math.min(n - 1, i + k); // Search for an uncaught thief within range for (int j = start; j <= end; j++) { if (arr[j] == 'T' && !caught[j]) { caught[j] = true; count++; break; } } } } return count; } public static void main(String[] args) { int k = 1; char[] arr = { 'P', 'T', 'T', 'P', 'T' }; System.out.println(catchThieves(arr, k)); } } Python # Python program to find the maximum number of thieves caught # Returns the maximum number of thieves that can be caught def catchThieves(arr, k): n = len(arr) # To mark if a thief is caught caught = [False] * n # Stores the number of caught thieves count = 0 for i in range(n): if arr[i] == 'P': start = max(0, i - k) end = min(n - 1, i + k) # Search for an uncaught thief within range for j in range(start, end + 1): if arr[j] == 'T' and not caught[j]: caught[j] = True count += 1 break return count if __name__ == "__main__": k = 1 arr = ['P', 'T', 'T', 'P', 'T'] print(catchThieves(arr, k)) C# using System; class GfG { // Returns the maximum number of thieves that can be caught static int catchThieves(char[] arr, int k) { int n = arr.Length; // To mark if a thief is caught bool[] caught = new bool[n]; // Stores the number of caught thieves int count = 0; for (int i = 0; i < n; i++) { if (arr[i] == 'P') { int start = Math.Max(0, i - k); int end = Math.Min(n - 1, i + k); // Search for an uncaught thief within range for (int j = start; j <= end; j++) { if (arr[j] == 'T' && !caught[j]) { caught[j] = true; count++; break; } } } } return count; } static void Main() { int k = 1; char[] arr = { 'P', 'T', 'T', 'P', 'T' }; Console.WriteLine(catchThieves(arr, k)); } } JavaScript // JavaScript program to find the maximum number of thieves caught // Returns the maximum number of thieves // that can be caught function catchThieves(arr, k) { let n = arr.length; // To mark if a thief is caught let caught = new Array(n).fill(false); // Stores the number of caught thieves let count = 0; for (let i = 0; i < n; i++) { if (arr[i] === 'P') { let start = Math.max(0, i - k); let end = Math.min(n - 1, i + k); // Search for an uncaught thief within range for (let j = start; j <= end; j++) { if (arr[j] === 'T' && !caught[j]) { caught[j] = true; count++; break; } } } } return count; } // Driver Code const k = 1; const arr = ['P', 'T', 'T', 'P', 'T']; console.log(catchThieves(arr, k)); Output2 [Expected Approach] Using Two Pointers - O(n) Time and O(1) SpaceThe main idea is to use two pointers (both begin from left side): one to track policemen and the other to track thieves. We traverse the array while maintaining these pointers. If a policeman and a thief are within the allowed range k, the thief is caught, and both pointers move forward. If the thief is too far left, we move the thief pointer forward; similarly, if the policeman is too far left, we move the policeman pointer forward.Step by Step implementation:Initialize Two Pointers: i for the next policeman ('P') and j for the next thief ('T'), along with a counter count = 0.Traverse the Array: Move i to the next 'P' and j to the next 'T'.Check Distance: If |i - j| ≤ k, catch the thief (count++), then move both pointers forward.Adjust Pointers: If the thief (j) is too far left, move j forward; if the policeman (i) is too far left, move i forward.Repeat Until End: Continue until either i or j reaches the end of the array. Return count. C++ // C++ program to find maximum number of thieves // caught #include <bits/stdc++.h> using namespace std; // Returns the maximum number of thieves // that can be caught using two pointers int catchThieves(vector<char> &arr, int k) { int n = arr.size(); // Two pointers for policemen and thieves int i = 0, j = 0; int count = 0; while (i < n && j < n) { // Move i to the next policeman while (i < n && arr[i] != 'P') i++; // Move j to the next thief while (j < n && arr[j] != 'T') j++; // If both policeman and thief exist and are within range k if (i < n && j < n && abs(i - j) <= k) { // Catch the thief count++; // Move to the next policeman i++; // Move to the next thief j++; } // If the thief is too far left, move the thief pointer else if (j < n && j < i) { j++; } // If the policeman is too far left, // move the policeman pointer else if (i < n && i < j) { i++; } } return count; } int main() { int k = 1; vector<char> arr = { 'P', 'T', 'T', 'P', 'T' }; cout<< catchThieves(arr, k) << endl; } Java // Java program to find maximum number of thieves // caught import java.util.*; public class GfG { // Returns the maximum number of thieves that // can be caught using two pointers static int catchThieves(char[] arr, int k) { int n = arr.length; // Two pointers for policemen and thieves int i = 0, j = 0; int count = 0; while (i < n && j < n) { // Move i to the next policeman while (i < n && arr[i] != 'P') i++; // Move j to the next thief while (j < n && arr[j] != 'T') j++; // If both policeman and thief exist // and are within range k if (i < n && j < n && Math.abs(i - j) <= k) { // Catch the thief count++; // Move to the next policeman i++; // Move to the next thief j++; } // If the thief is too far left, // move the thief pointer else if (j < n && j < i) { j++; } // If the policeman is too far left, // move the policeman pointer else if (i < n && i < j) { i++; } } return count; } public static void main(String[] args) { int k = 1; char[] arr = { 'P', 'T', 'T', 'P', 'T' }; System.out.println(catchThieves(arr, k)); } } Python # Python program to find the maximum number of thieves caught # Returns the maximum number of thieves # that can be caught using two pointers def catchThieves(arr, k): n = len(arr) # Two pointers for policemen and thieves i, j = 0, 0 count = 0 while i < n and j < n: # Move i to the next policeman while i < n and arr[i] != 'P': i += 1 # Move j to the next thief while j < n and arr[j] != 'T': j += 1 # If both policeman and thief exist # and are within range k if i < n and j < n and abs(i - j) <= k: # Catch the thief count += 1 # Move to the next policeman i += 1 # Move to the next thief j += 1 # If the thief is too far left, # move the thief pointer elif j < n and j < i: j += 1 # If the policeman is too far left, # move the policeman pointer elif i < n and i < j: i += 1 return count if __name__ == "__main__": k = 1 arr = ['P', 'T', 'T', 'P', 'T'] print(catchThieves(arr, k)) C# using System; class GfG { static int catchThieves(char[] arr, int k) { int n = arr.Length; // Two pointers for policemen and thieves int i = 0, j = 0; int count = 0; while (i < n && j < n) { // Move i to the next policeman while (i < n && arr[i] != 'P') i++; // Move j to the next thief while (j < n && arr[j] != 'T') j++; // If both policeman and thief exist // and are within range k if (i < n && j < n && Math.Abs(i - j) <= k) { // Catch the thief count++; // Move to the next policeman i++; // Move to the next thief j++; } // If the thief is too far left, move the thief pointer else if (j < n && j < i) { j++; } // If the policeman is too far left, // move the policeman pointer else if (i < n && i < j) { i++; } } return count; } static void Main() { int k = 1; char[] arr = { 'P', 'T', 'T', 'P', 'T' }; Console.WriteLine(catchThieves(arr, k)); } } JavaScript // JavaScript program to find the maximum number of thieves caught // Returns the maximum number of thieves // that can be caught function catchThieves(arr, k) { let n = arr.length; // Two pointers for policemen and thieves let i = 0, j = 0; let count = 0; while (i < n && j < n) { // Move i to the next policeman while (i < n && arr[i] !== 'P') i++; // Move j to the next thief while (j < n && arr[j] !== 'T') j++; // If both policeman and thief exist and are within range k if (i < n && j < n && Math.abs(i - j) <= k) { // Catch the thief count++; // Move to the next policeman i++; // Move to the next thief j++; } // If the thief is too far left, move the thief pointer else if (j < n && j < i) { j++; } // If the policeman is too far left, move the policeman pointer else if (i < n && i < j) { i++; } } return count; } // Driver Code const k = 1; const arr = ['P', 'T', 'T', 'P', 'T']; console.log(catchThieves(arr, k)); Output2 Comment More infoAdvertise with us Next Article Fitting Shelves Problem R RSatish Improve Article Tags : Greedy DSA National Instruments two-pointer-algorithm Practice Tags : National InstrumentsGreedytwo-pointer-algorithm Similar Reads Greedy Algorithms Greedy 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 Greedy Algorithm Tutorial Greedy is an algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most obvious and immediate benefit. Greedy algorithms are used for optimization problems. An optimization problem can be solved using Greedy if the problem has the following pro 9 min read Greedy Algorithms General Structure A greedy algorithm solves problems by making the best choice at each step. Instead of looking at all possible solutions, it focuses on the option that seems best right now.Example of Greedy Algorithm - Fractional KnapsackProblem structure:Most of the problems where greedy algorithms work follow thes 5 min read Difference between Greedy Algorithm and Divide and Conquer Algorithm Greedy algorithm and divide and conquer algorithm are two common algorithmic paradigms used to solve problems. The main difference between them lies in their approach to solving problems. Greedy Algorithm:The greedy algorithm is an algorithmic paradigm that follows the problem-solving heuristic of m 3 min read Greedy Approach vs Dynamic programming Greedy approach and Dynamic programming are two different algorithmic approaches that can be used to solve optimization problems. Here are the main differences between these two approaches: Greedy Approach:The greedy approach makes the best choice at each step with the hope of finding a global optim 2 min read Comparison among Greedy, Divide and Conquer and Dynamic Programming algorithm Greedy algorithm, divide and conquer algorithm, and dynamic programming algorithm are three common algorithmic paradigms used to solve problems. Here's a comparison among these algorithms:Approach:Greedy algorithm: Makes locally optimal choices at each step with the hope of finding a global optimum. 4 min read Standard Greedy algorithmsActivity Selection Problem | Greedy Algo-1Given n activities with start times in start[] and finish times in finish[], find the maximum number of activities a single person can perform without overlap. A person can only do one activity at a time. Examples: Input: start[] = [1, 3, 0, 5, 8, 5], finish[] = [2, 4, 6, 7, 9, 9]Output: 4Explanatio 13 min read Job Sequencing ProblemGiven two arrays: deadline[] and profit[], where the index of deadline[] represents a job ID, and deadline[i] denotes the deadline for that job and profit[i] represents profit of doing ith job. Each job takes exactly one unit of time to complete, and only one job can be scheduled at a time. A job ea 13 min read Huffman Coding | Greedy Algo-3Huffman coding is a lossless data compression algorithm. The idea is to assign variable-length codes to input characters, lengths of the assigned codes are based on the frequencies of corresponding characters. The variable-length codes assigned to input characters are Prefix Codes, means the codes ( 12 min read Huffman DecodingWe have discussed Huffman Encoding in a previous post. In this post, decoding is discussed. Examples: Input Data: AAAAAABCCCCCCDDEEEEEFrequencies: A: 6, B: 1, C: 6, D: 2, E: 5 Encoded Data: 0000000000001100101010101011111111010101010 Huffman Tree: '#' is the special character usedfor internal nodes 15 min read Water Connection ProblemYou are given n houses in a colony, numbered from 1 to n, and p pipes connecting these houses. Each house has at most one outgoing pipe and at most one incoming pipe. Your goal is to install tanks and taps efficiently.A tank is installed at a house that has one outgoing pipe but no incoming pipe.A t 8 min read Greedy Algorithm for Egyptian FractionEvery positive fraction can be represented as sum of unique unit fractions. A fraction is unit fraction if numerator is 1 and denominator is a positive integer, for example 1/3 is a unit fraction. Such a representation is called Egyptian Fraction as it was used by ancient Egyptians. Following are a 11 min read Policemen catch thievesGiven an array arr, where each element represents either a policeman (P) or a thief (T). The objective is to determine the maximum number of thieves that can be caught under the following conditions:Each policeman (P) can catch only one thief (T).A policeman can only catch a thief if the distance be 12 min read Fitting Shelves ProblemGiven length of wall w and shelves of two lengths m and n, find the number of each type of shelf to be used and the remaining empty space in the optimal solution so that the empty space is minimum. The larger of the two shelves is cheaper so it is preferred. However cost is secondary and first prior 9 min read Assign Mice to HolesThere are N Mice and N holes are placed in a straight line. Each hole can accommodate only 1 mouse. A mouse can stay at his position, move one step right from x to x + 1, or move one step left from x to x -1. Any of these moves consumes 1 minute. Assign mice to holes so that the time when the last m 8 min read Greedy algorithm on ArrayMinimum product subset of an arrayINTRODUCTION: The minimum product subset of an array refers to a subset of elements from the array such that the product of the elements in the subset is minimized. To find the minimum product subset, various algorithms can be used, such as greedy algorithms, dynamic programming, and branch and boun 13 min read Maximize array sum after K negations using SortingGiven an array of size n and an integer k. We must modify array k number of times. In each modification, we can replace any array element arr[i] by -arr[i]. The task is to perform this operation in such a way that after k operations, the sum of the array is maximum.Examples : Input : arr[] = [-2, 0, 10 min read Minimum sum of product of two arraysFind the minimum sum of Products of two arrays of the same size, given that k modifications are allowed on the first array. In each modification, one array element of the first array can either be increased or decreased by 2.Examples: Input : a[] = {1, 2, -3} b[] = {-2, 3, -5} k = 5 Output : -31 Exp 14 min read Minimum sum of absolute difference of pairs of two arraysGiven two arrays a[] and b[] of equal length n. The task is to pair each element of array a to an element in array b, such that sum S of absolute differences of all the pairs is minimum.Suppose, two elements a[i] and a[j] (i != j) of a are paired with elements b[p] and b[q] of b respectively, then p 7 min read Minimum increment/decrement to make array non-IncreasingGiven an array a, your task is to convert it into a non-increasing form such that we can either increment or decrement the array value by 1 in the minimum changes possible. Examples : Input : a[] = {3, 1, 2, 1}Output : 1Explanation : We can convert the array into 3 1 1 1 by changing 3rd element of a 11 min read Sorting array with reverse around middleConsider the given array arr[], we need to find if we can sort array with the given operation. The operation is We have to select a subarray from the given array such that the middle element(or elements (in case of even number of elements)) of subarray is also the middle element(or elements (in case 6 min read Sum of Areas of Rectangles possible for an arrayGiven an array, the task is to compute the sum of all possible maximum area rectangles which can be formed from the array elements. Also, you can reduce the elements of the array by at most 1. Examples: Input: a = {10, 10, 10, 10, 11, 10, 11, 10} Output: 210 Explanation: We can form two rectangles o 13 min read Largest lexicographic array with at-most K consecutive swapsGiven an array arr[], find the lexicographically largest array that can be obtained by performing at-most k consecutive swaps. Examples : Input : arr[] = {3, 5, 4, 1, 2} k = 3 Output : 5, 4, 3, 2, 1 Explanation : Array given : 3 5 4 1 2 After swap 1 : 5 3 4 1 2 After swap 2 : 5 4 3 1 2 After swap 3 9 min read Partition into two subsets of lengths K and (N - k) such that the difference of sums is maximumGiven an array of non-negative integers of length N and an integer K. Partition the given array into two subsets of length K and N - K so that the difference between the sum of both subsets is maximum. Examples : Input : arr[] = {8, 4, 5, 2, 10} k = 2 Output : 17 Explanation : Here, we can make firs 7 min read Greedy algorithm on Operating SystemProgram for First Fit algorithm in Memory ManagementPrerequisite : Partition Allocation MethodsIn the first fit, the partition is allocated which is first sufficient from the top of Main Memory.Example : Input : blockSize[] = {100, 500, 200, 300, 600}; processSize[] = {212, 417, 112, 426};Output:Process No. Process Size Block no. 1 212 2 2 417 5 3 11 8 min read Program for Best Fit algorithm in Memory ManagementPrerequisite : Partition allocation methodsBest fit allocates the process to a partition which is the smallest sufficient partition among the free available partitions. Example: Input : blockSize[] = {100, 500, 200, 300, 600}; processSize[] = {212, 417, 112, 426}; Output: Process No. Process Size Bl 8 min read Program for Worst Fit algorithm in Memory ManagementPrerequisite : Partition allocation methodsWorst Fit allocates a process to the partition which is largest sufficient among the freely available partitions available in the main memory. If a large process comes at a later stage, then memory will not have space to accommodate it. Example: Input : blo 8 min read Program for Shortest Job First (or SJF) CPU Scheduling | Set 1 (Non- preemptive)The shortest job first (SJF) or shortest job next, is a scheduling policy that selects the waiting process with the smallest execution time to execute next. SJN, also known as Shortest Job Next (SJN), can be preemptive or non-preemptive.  Characteristics of SJF Scheduling: Shortest Job first has th 13 min read Job Scheduling with two jobs allowed at a timeGiven a 2d array jobs[][] of order n * 2, where each element jobs[i], contains two integers, representing the start and end time of the job. Your task is to check if it is possible to complete all the jobs, provided that two jobs can be done simultaneously at a particular moment. Note: If a job star 6 min read Optimal Page Replacement AlgorithmIn operating systems, whenever a new page is referred and not present in memory, page fault occurs, and Operating System replaces one of the existing pages with newly needed page. Different page replacement algorithms suggest different ways to decide which page to replace. The target for all algorit 3 min read Greedy algorithm on GraphPrimâs Algorithm for Minimum Spanning Tree (MST)Primâs algorithm is a Greedy algorithm like Kruskal's algorithm. This algorithm always starts with a single node and moves through several adjacent nodes, in order to explore all of the connected edges along the way.The algorithm starts with an empty spanning tree. The idea is to maintain two sets o 15+ min read Boruvka's algorithm | Greedy Algo-9We have discussed the following topics on Minimum Spanning Tree.Applications of Minimum Spanning Tree Problem Kruskalâs Minimum Spanning Tree Algorithm Primâs Minimum Spanning Tree AlgorithmIn this post, Boruvka's algorithm is discussed. Like Prim's and Kruskal's, Boruvkaâs algorithm is also a Greed 15+ min read Dial's Algorithm (Optimized Dijkstra for small range weights)Given a weighted Graph and a source vertex, the task is to find the shortest paths from the source node to all other vertices.Example:Input : n = 9, src = 0Output : 0 4 12 19 21 11 9 8 14 We have learned about how to find the shortest path from a given source vertex to all other vertex using Dijkstr 10 min read Minimum cost to connect all citiesThere are n cities and there are roads in between some of the cities. Somehow all the roads are damaged simultaneously. We have to repair the roads to connect the cities again. There is a fixed cost to repair a particular road.Input is in the form of edges {u, v, w} where, u and v are city indices. 7 min read Number of single cycle components in an undirected graphGiven a set of 'n' vertices and 'm' edges of an undirected simple graph (no parallel edges and no self-loop), find the number of single-cycle components present in the graph. A single-cyclic component is a graph of n nodes containing a single cycle through all nodes of the component. Example: Let us 9 min read Like