CPP Programming Using STL in Competitive Programming

Introduction

Competitive programming is a domain where programmers solve complex algorithmic problems under time_constraints. To excel in competitive programming, utilizing efficient and optimized code is crucial. The Standard Template Library (STL) in C++ provides a suite of tools that can significantly enhance the efficiency and readability of the code. Here, we will delve into the essential components of STL that are most beneficial in competitive programming, detailing their functionalities and providing relevant examples.

1. Containers

Containers in STL are used to store, organize, and manage groups of elements. They offer different storage and retrieval semantics optimized for various use cases.

• Sequences

  • std::vector: Dynamic array.

    vector<int> v;        // Declare a vector of integers.
    v.push_back(10);      // Add elements.
    cout << v[0];         // Access elements.
    

  • std::list: Doubly linked list.

    list<int> lst;        // Declare a list of integers.
    lst.push_back(20);    // Add elements.
    lst.push_front(10);
    

  • std::deque: Double-ended queue.

    deque<int> dq;        // Declare a deque of integers.
    dq.push_back(30);     // Add elements.
    dq.push_front(20);
    

• Associative

  • std::set: Red-black tree storing unique elements in sorted order.

    set<int> s;           // Declare a set of integers.
    s.insert(10);         // Add elements.
    s.insert(20);
    

  • std::multiset: Red-black tree storing multiple elements in sorted order.

    multiset<int> ms;     // Declare a multiset of integers.
    ms.insert(10);        // Add elements.
    ms.insert(10);
    

  • std::map: Red-black tree storing key-value pairs, keys unique and sorted.

    map<string, int> mp;  // Declare a map with string keys and integer values.
    mp["apple"] = 1;      // Add key-value pairs.
    mp["banana"] = 2;
    

  • std::multimap: Red-black tree storing multiple key-value pairs, keys can repeat.

    multimap<string, int> mmp; // Declare a multimap with string keys and integer values.
    mmp.insert({"apple", 1});  // Add key-value pairs.
    mmp.insert({"apple", 2});
    

• Unordered Associative

  • std::unordered_set: Hash table storing unique elements.

    unordered_set<int> us; // Declare an unordered set of integers.
    us.insert(30);         // Add elements.
    us.insert(40);
    

  • std::unordered_multiset: Hash table storing multiple elements.

    unordered_multiset<int> ums; // Declare an unordered multiset of integers.
    ums.insert(30);            // Add elements.
    ums.insert(30);
    

  • std::unordered_map: Hash table storing key-value pairs.

    unordered_map<string,int> ump; // Declare an unordered map with string keys and integer values.
    ump["orange"] = 3;           // Add key-value pairs.
    ump["peach"] = 4;
    

  • std::unordered_multimap: Hash table storing multiple key-value pairs.

    unordered_multimap<string,int> ummp; // Declare an unordered multimap with string keys and integer values.
    ummp.insert({"orange", 3});          // Add key-value pairs.
    ummp.insert({"orange", 3});
    

2. Algorithms

Algorithms in STL provide implementations for common algorithms like sorting, searching, and more.

• Sorting

  • std::sort: Sort elements in a range.

    vector<int> v{5, 4, 3, 2, 1};
    sort(v.begin(), v.end());           // Sort in ascending order.
    sort(v.begin(), v.end(), greater<int>()); // Sort in descending order.
    

• Searching

  • std::find: Find an element in a range.

    auto pos = find(v.begin(), v.end(), 3);     // Find the element '3'.
    if (pos != v.end()) cout << "Element found"; // Check if element is found.
    

• Binary Search

  • std::binary_search, std::lower_bound, std::upper_bound: Efficient search in sorted ranges.

    bool found = binary_search(v.begin(), v.end(), 3); // true if '3' is found.
    auto lower = lower_bound(v.begin(), v.end(), 3);     // First element not less than '3'.
    auto upper = upper_bound(v.begin(), v.end(), 3);     // First element greater than '3'.
    

3. Iterators

Iterators provide a unified way to access elements in containers.

• Traversing Vectors

  vector<int> v{ 1, 2, 3, 4, 5 };
  for(auto it = v.begin(); it != v.end(); ++it) // Standard iterator.
      cout << *it << " ";
  for(auto it = v.rbegin(); it != v.rend(); ++it) // Reverse iterator.
      cout << *it << " ";
  

• Traversing Maps

  map<string, int> mp{ {"apple", 1}, {"banana", 2} };
  for(auto it = mp.begin(); it != mp.end(); ++it) // Iterating map.
      cout << it->first << " " << it->second << " ";
  

4. Function Objects and Lambda Expressions

Function objects and lambda expressions provide a way to create simple, ad-hoc functions.

• Function Objects

  plus<int> pl;    // Function object to add two integers.
  int sum = pl(3, 4); // sum = 7.
  plus<> plu;      // Generic function object.
  double dsum = plu(3.0, 4.5); // dsum = 7.5.
  

• Lambda Expressions

  auto lambda = [] (int a, int b) { return a*b; }; // Lambda to multiply two numbers.
  int product = lambda(2, 3);   // product = 6.
  

Conclusion

Mastering STL in C++ is pivotal for competitive programmers as it provides powerful data structures and algorithms that can handle a variety of problem scenarios efficiently. Proficient use of STL not only speeds up coding but also improves the reliability and correctness of the code. With practice and understanding, competitive programmers can leverage STL to advance their problem-solving skills and strategies.

CPP Programming Using STL in Competitive Programming: Examples, Set Route, and Run the Application Then Data Flow — Step-by-Step for Beginners

Competitive programming often involves solving problems efficiently, and the Standard Template Library (STL) in C++ can significantly facilitate this process. The STL is a powerful toolkit that offers a wide range of pre-written algorithms and data structures to solve common programming challenges. However, for beginners, it might be overwhelming to start using these tools effectively. This guide aims to provide a clear walkthrough, including setting up your environment, a step-by-step approach to writing code, and understanding data flow, specifically tailored to competitive programming using STL.

Setting Up Your Environment:

Before diving into code, ensure you have a working C++ environment. Here's a simple setup route you can follow:

1. Install a Compiler: GCC (GNU Compiler Collection) is a very popular compiler for C++. It can be installed via package managers on Linux and macOS, or downloaded from the official website and set up on Windows.

2. Code Editor/IDE: Choose an integrated development environment (IDE) or code editor with support for C++. Recommended options include:

   • Visual Studio Code (VS Code): Highly customizable and supports extensions for better C++ experience.
   • Code::Blocks: Free, open-source IDE.
   • CLion: A paid IDE by JetBrains but offers powerful features for debugging and testing.
   • Online Compilers: Websites like CodeChef, LeetCode, and Codeforces offer online compilers where you can write and execute code directly on their platforms.

Once you've chosen your tools, proceed with the following steps to effectively leverage STL in competitive programming.

Writing Code Using STL:

Let’s consider a simple problem statement to demonstrate using STL effectively in competitive programming: Given an array of integers, output the maximum and minimum elements.

Example Problem and Solution:

Let's solve this using C++ and STL:

#include <iostream>
#include <vector>
#include <algorithm>

int main(){
    int n;
    std::cin >> n; // Read the number of integers
    std::vector<int> vec(n); // Create a vector of size n
    
    // Input elements into the vector
    for(auto &i : vec){
        std::cin >> i;
    }
    
    // Use STL functions to find min and max
    auto it_min = std::min_element(vec.begin(), vec.end());
    auto it_max = std::max_element(vec.begin(), vec.end());
    
    // Output the results
    std::cout << "Minimum element: " << *it_min << "\n";
    std::cout << "Maximum element: " << *it_max << "\n";
    
    return 0;
}

Explanation of the Code:

1. Input Handling:

   • The program starts by reading an integer n which indicates the number of elements in the array.
   • A vector vec of size n is created using std::vector<int> vec(n);. This dynamically allocates an array of integers.

2. Reading Data into Vector:

   • A range-based for loop is used, denoted by for(auto &i : vec), to input each integer i into the vector. The reference & ensures that we modify the actual elements of the vector.

3. Using STL’s Algorithms:

   • std::min_element and std::max_element are two STL functions used to find the iterators pointing to the minimum and maximum elements in the container, respectively.
   • These functions take two arguments: the beginning (vec.begin()) and end (vec.end()) of the container and return an iterator to the desired position.

4. Output:

   • Dereferencing the iterators obtained from the STL functions (*it_min and *it_max) gives us the minimum and maximum values, which are then printed to the console.

Running the Application:

Assuming you are using an online compiler on Codeforces or another platform, the execution can be handled automatically. You just need to paste the code and submit it as is. If you are running locally, follow these steps:

1. Save the code in a file with a .cpp extension, e.g., minmax_stl.cpp.

2. Open your terminal or command prompt.

3. Navigate to the directory where you saved your file.

4. Compile your code using GCC. Type the following command:

   g++ -std=c++11 -O2 minmax_stl.cpp -o minmax_stl
   

   This command tells the compiler to use C++11 standards, optimize the output with -O2, and save the result in an executable named minmax_stl.

5. Run the executable:

   ./minmax_stl
   

6. Provide sample inputs to test your solution.

Understanding Data Flow:

Understanding data flow helps you debug issues and optimize your solution. Let’s break down the data flow in the provided example:

1. Input: The program reads an integer n followed by n integers from standard input (std::cin).
2. Storage: These integers are stored in a dynamic array implemented via the std::vector<int> class.
3. Processing:
   • The std::min_element function iterates over the vector starting from the first element (vec.begin()) up to the last element (vec.end()).
   • It does a pairwise comparison and keeps track of the smallest element found, returning an iterator to that element.
   • Similarly, std::max_element works to find the largest element.
4. Output: The values at the positions pointed by these iterators are dereferenced using * and printed to the standard output (std::cout).

Common Data Structures in STL for Competitive Programming:

While vectors are used above, STL offers several other data structures that are frequently used in competitive programming:

1. Arrays: Use std::array when you know the fixed size of container beforehand.
2. Deques: std::deque (double-ended queue) provides fast insertion and deletion from both ends of the container.
3. Linked Lists: std::list and std::forward_list are singly and doubly linked lists, offering different operations based on their implementation.
4. Sets and Maps: std::set, std::unordered_set, std::map, std::unordered_map are collections with fast lookup times.
5. Queues and Stacks: std::queue, std::stack are first-in-first-out and last-in-first-out containers.
6. Priority Queues: std::priority_queue can maintain elements in sorted order and always allow access to the largest element.

To become proficient in competitive programming using STL, regularly practice coding problems that utilize these various data structures and algorithms. Websites like LeetCode, TopCoder, HackerRank, and Codeforces can provide a wealth of problems ranging from beginner to advanced levels to hone your skills.

Conclusion:

Competitive programming using STL can greatly enhance your problem-solving capabilities by providing efficient and tested implementations of common algorithms and data structures. By following the steps outlined here — setting up your environment, writing code, running applications, and understanding data flow — you'll be well on your way to mastering STL in C++. Always keep practicing and experimenting with different functions, and don’t hesitate to refer to official STL documentation and online resources for more in-depth learning. Happy coding!

Top 10 Questions and Answers on C++ Programming Using STL in Competitive Programming

1. What are the benefits of using STL in competitive programming?

Answer: The Standard Template Library (STL) is an invaluable asset in competitive programming due to several key benefits:

• Time Efficiency: STL provides highly optimized, pre-tested, and well-documented components ready for use, saving precious time during competitions.
• Reduced Bugs: Since these components have been extensively tested and used by many developers, they reduce the likelihood of bugs in your code.
• Standardization: Using STL ensures uniformity across different compilers and platforms, facilitating code portability.
• Scope for Improvement: Familiarity with STL can help contestants write more efficient and cleaner code, often leading to better ranks.

2. How do you use the vector in STL for dynamic arrays?

Answer: In competitive programming, std::vector is often used for dynamic arrays due to its flexibility and ease of use. Here's a basic overview of some of its functionalities:

• Declaration: std::vector<int> v; declares an empty vector of integers.
• Adding Elements: v.push_back(5); adds 5 to the end of the vector.
• Accessing Elements: v[i] gives you access to the element at the i-th index (zero-based index).
• Size: v.size() returns the number of elements in the vector.
• Iterators: You can iterate over vectors using iterators as follows:

  for(auto it = v.begin(); it != v.end(); it++) {
      // Do something with *it
  }
  

• Range-based For Loop: for(auto &x : v) is a more concise way to iterate over all elements in v.

3. Describe the difference between set and unordered_set in STL.

Answer: Both std::set and std::unordered_set are used to store unique elements, but they differ in implementation and performance characteristics:

• std::set:

  • Based on a Red-Black Tree, making it ordered.
  • Provides logarithmic time complexity for insertion, deletion, and lookup (O(log n)).
  • Elements are stored in sorted order, allowing for ordered traversal via iterators.
  • Suitable when order is important.

• std::unordered_set:

  • Implemented using a hash table.
  • Offers average constant time complexity (O(1)) for insertion, deletion, and lookup operations.
  • Does not maintain any specific order of elements, so traversal order is arbitrary.
  • More efficient than std::set when order is irrelevant and performance is critical.

4. How can you efficiently sort a vector in STL?

Answer: Sorting a vector in C++ STL can be efficiently done using the std::sort algorithm, which implements introsort (a hybrid sorting algorithm derived from quicksort, heapsort, and insertion sort).

• Basic Usage: std::sort(v.begin(), v.end()); sorts the vector v in ascending order.
• Descending Order: std::sort(v.begin(), v.end(), std::greater<int>()); sorts the vector in descending order.
• Custom Comparator: You can define your own comparator function or lambda expression to sort based on a custom criterion.

  std::sort(v.begin(), v.end(), [](int a, int b) {
      return (a % 10) < (b % 10); // Sort based on the last digit
  });
  

• Partial Sort: If you only need the k smallest or largest elements, std::partial_sort can be used more efficiently.

5. Explain how you would use priority_queue in competitive programming.

Answer: std::priority_queue is a container adapter that provides constant time access to the largest (by default) element, and logarithmic time for insertions or deletions of the largest element. It is particularly useful in competitive programming for solving problems involving:

• Dijkstra’s Shortest Path Algorithm: std::priority_queue<std::pair<int, int>, std::vector<std::pair<int, int>>, std::greater<std::pair<int, int>>> pq; can be used to fetch the least weighted node.
• Kth Largest Element: Efficiently finding the kth largest/smallest element in a stream.
• Processed Tasks: Managing tasks based on priority.

Basic Operations:

• Push: pq.push({distance, node}); inserts a new element.
• Top: int top_node = pq.top().second; retrieves the element with highest priority.
• Pop: pq.pop(); removes the top element.

6. What is the use of map and unordered_map in competitive programming?

Answer: Both std::map and std::unordered_map are used to store key-value pairs, but they differ significantly in internal implementation and performance characteristics:

• std::map:

  • Implemented as a balanced tree (typically Red-Black Tree).
  • Provides logarithmic time complexity (O(log n)) for insertions, deletions, and lookups.
  • Maintains keys in sorted order, allowing for ordered traversal.
  • Suitable for scenarios requiring elements to remain sorted during insertions and deletions.

• std::unordered_map:

  • Implemented as a hash table.
  • Offers average constant time complexity (O(1)) for insertions, deletions, and lookups.
  • Does not maintain any order; keys are stored based on hash values.
  • Faster lookups and insertions due to hashing, but unordered traversal is possible.

7. How do you use the deque in STL for double-ended queues?

Answer: std::deque (double-ended queue) is a dynamic array that allows fast insertion and deletion of elements at both the beginning and the end. It is particularly useful when individual elements need to be accessed at both ends efficiently.

• Declaration: std::deque<int> dq; creates an empty deque of integers.
• Adding Elements:
  • dq.push_back(5); adds 5 to the end of the deque.
  • dq.push_front(3); adds 3 to the front of the deque.
• Accessing Elements:
  • dq[0] accesses the first element.
  • dq.back() accesses the last element.
• Removing Elements:
  • dq.pop_back(); removes the last element.
  • dq.pop_front(); removes the first element.
• Iterators: Use iterators to traverse the deque as with other containers.

Use Cases:

• Sliding Window: Optimizing operations required near both ends.
• Implementing Queues and Stacks Simultaneously: Where elements need to be added/removed from both ends.
• Breadth-First Search (BFS): Efficiently managing elements to be explored next.

8. What is the difference between upper_bound and lower_bound in STL?

Answer: std::lower_bound and std::upper_bound are algorithms that perform binary search to find specific positions within a sorted range (or within containers like std::set and std::map). Understanding their differences is crucial for efficient problem-solving in competitive programming.

• std::lower_bound:

  • Returns an iterator pointing to the first element that is not less than the given value.
  • This means it finds the first element that is greater than or equal to the value.
  • Use Case: Finding the position to insert an element to maintain sorted order while allowing duplicates.

    auto it = std::lower_bound(v.begin(), v.end(), value);
    

• std::upper_bound:

  • Returns an iterator pointing to the first element that is greater than the given value.
  • Use Case: Finding the position to insert a new element when you want all existing equal elements to appear before it.

    auto it = std::upper_bound(v.begin(), v.end(), value);
    

Key Differences:

• lower_bound includes equals (>=), while upper_bound excludes (>).
• If the value is present in the container, std::lower_bound points to the first occurrence, while std::upper_bound points to the position right after all occurrences.

Example Usage:

std::vector<int> v = {1, 2, 2, 4, 5};
auto it_low = std::lower_bound(v.begin(), v.end(), 2); // Points to first 2
auto it_upp = std::upper_bound(v.begin(), v.end(), 2); // Points to 4

9. How can you use bitset for bitmasking and bitset operations?

Answer: std::bitset is a specialized container that represents a fixed-size sequence of bits, which can be used for efficient bit manipulation and bitmasking operations. It is particularly useful in competitive programming for problems involving:

• Subset Generation: Enumerating all subsets of a set.
• State Representation: Encoding states in dynamic programming or graph algorithms.
• Flags and Switches: Managing multiple boolean flags in a compact manner.

Key Features:

• Fixed Size: The bitset size must be known at compile time.
• Element Access: Accessing individual bits using bitset[n].
• Bitwise Operations: Supports bitwise operations like &, |, ^, ~, <<, >>.
• Counting and Positions:
  • bitset.count(): Returns the number of 1 bits.
  • bitset.any(): Checks if any bit is set to 1.
  • bitset.none(): Checks if all bits are 0.
  • bitset.test(n): Checks if the n-th bit is set to 1.
  • bitset.set(n, b): Sets the n-th bit to b (default 1).

Example Usage:

#include <bitset>
#include <iostream>

int main() {
    std::bitset<8> b("11001010");
    std::cout << "Original: " << b << std::endl;
    std::cout << "Count of 1s: " << b.count() << std::endl;

    b.set(4, 1); // Set 5th bit (0-based index)
    std::cout << "After set(4, 1): " << b << std::endl;

    b.reset(3); // Reset 4th bit (0-based index)
    std::cout << "After reset(3): " << b << std::endl;

    b.flip(); // Flip all bits
    std::cout << "After flip: " << b << std::endl;

    bool any_set = b.any();
    std::cout << "Is any bit set? " << any_set << std::endl;

    bool none_set = b.none();
    std::cout << "Are all bits 0? " << none_set << std::endl;

    return 0;
}

Output:

Original: 11001010
Count of 1s: 4
After set(4, 1): 11011010
After reset(3): 11010010
After flip: 00101101
Is any bit set? 1
Are all bits 0? 0

Benefits in Competitive Programming:

• Efficient Bitwise Operations: Faster than manually implementing bit operations on integers.
• Conciseness and Readability: Makes the code more readable and easier to maintain.
• Boundary Checks: Automatically handles bit positions within the bitset size.

10. When should you use stack and queue in STL, and what are the differences?

Answer: std::stack and std::queue are container adapters in STL that provide convenient ways to implement stack and queue data structures, respectively. Understanding when to use each and their differences is essential for solving various competitive programming problems efficiently.

Stack (std::stack)

Definition: A stack is a linear data structure that follows the Last In First Out (LIFO) principle, where elements can only be added or removed from the top.

Key Features:

• Push: Adds an element to the top of the stack. Time Complexity: O(1).
• Pop: Removes the element from the top of the stack. Time Complexity: O(1).
• Top: Accesses the top element without removing it. Time Complexity: O(1).

Use Cases:

• Function Call Management: Simulating function calls and recursion.
• Expression Evaluation: Parsing mathematical expressions (infix to postfix conversion, evaluating postfix expressions).
• Backtracking Algorithms: Implementing algorithms like DFS or backtracking to explore possibilities.
• Undo Mechanisms: Managing undo operations in applications.

Example:

#include <stack>
#include <iostream>

int main() {
    std::stack<int> st;
    st.push(10);
    st.push(20);
    st.push(30);

    std::cout << "Top element: " << st.top() << std::endl;
    st.pop();
    std::cout << "Top element after pop: " << st.top() << std::endl;

    return 0;
}

Output:

Top element: 30
Top element after pop: 20

Queue (std::queue)

Definition: A queue is a linear data structure that follows the First In First Out (FIFO) principle, where elements can only be added at the rear and removed from the front.

Key Features:

• Push: Adds an element to the back of the queue. Time Complexity: O(1).
• Pop: Removes the element from the front of the queue. Time Complexity: O(1).
• Front: Accesses the front element without removing it. Time Complexity: O(1).
• Back: Accesses the back element without removing it. Time Complexity: O(1).

Use Cases:

• Breadth-First Search (BFS): Efficiently exploring graph nodes level by level.
• Task Scheduling: Managing tasks or events that need to be processed in the order they arrive.
• Buffer Management: Implementing buffers for data streams.
• Sliding Window Algorithms: Efficiently processing a sliding window over a data set.

Example:

#include <queue>
#include <iostream>

int main() {
    std::queue<int> q;
    q.push(10);
    q.push(20);
    q.push(30);

    std::cout << "Front element: " << q.front() << std::endl;
    std::cout << "Back element: " << q.back() << std::endl;
    q.pop();
    std::cout << "Front element after pop: " << q.front() << std::endl;

    return 0;
}

Output:

Front element: 10
Back element: 30
Front element after pop: 20

Differences and When to Use Them

| Feature | std::stack | std::queue | |---------------|------------------------------|------------------------------| | Principle | Last In First Out (LIFO) | First In First Out (FIFO) | | Core Operations| Push (top), Pop (top) | Push (back), Pop (front) | | Time Complexity| Push, Pop, Top: O(1) | Push, Pop, Front, Back: O(1)| | Use Cases | Recursion, Expression Evaluation, DFS| BFS, Task Scheduling, Sliding Window| | Real-world Analogy| Stack of plates, Calls Stack| Line for a service|

Choosing Between Stack and Queue

• Use std::stack when the order of operations requires processing the most recent element first (e.g., function call stack, undo mechanisms).
• Use std::queue when elements need to be processed in the order they were added (e.g., BFS, task scheduling).

Example Problem: Evaluating Postfix Expressions with std::stack

Problem Statement: Evaluate a given postfix expression using a stack.

Approach:

• Operands: Push them onto the stack.
• Operators: Pop the top two operands from the stack, apply the operator, and push the result back onto the stack.
• Final Result: The stack should contain one element, which is the result of the postfix expression.

Implementation:

#include <stack>
#include <iostream>
#include <sstream>

int evaluatePostfix(const std::string &expression) {
    std::stack<int> st;
    std::istringstream iss(expression);
    std::string token;

    while (iss >> token) {
        if (isdigit(token[0])) {
            st.push(std::stoi(token));
        } else {
            int operand2 = st.top(); st.pop();
            int operand1 = st.top(); st.pop();
            int result;

            switch (token[0]) {
                case '+': result = operand1 + operand2; break;
                case '-': result = operand1 - operand2; break;
                case '*': result = operand1 * operand2; break;
                case '/': result = operand1 / operand2; break;
                default: throw std::runtime_error("Invalid operator");
            }

            st.push(result);
        }
    }

    return st.top();
}

int main() {
    std::string expression = "3 4 + 2 * 7 /";
    int result = evaluatePostfix(expression);
    std::cout << "Result: " << result << std::endl;

    return 0;
}

Output:

Result: 2

Summary

Mastering the Standard Template Library (STL) components in C++ is crucial for success in competitive programming. Each STL component—such as vector, set, map, stack, queue, bitset, and priority_queue—provides powerful tools and optimizations that can significantly enhance the efficiency and correctness of your code. Understanding when and how to use these components effectively is key to solving complex problems within the time and memory constraints typical of competitive programming environments.