Limitations of Arrays

1. Fixed size – cannot be resized after declaration.
2. No bounds checking – accessing out-of-range elements may lead to undefined behavior.
3. Inefficient insertion/deletion compared to other data structures.
4. Wasted memory if allocated size is too large.
5. Does not support built-in dynamic features like lists.
6. Difficult to manage multi-dimensional arrays in complex logic.

Advantages and Applications of Arrays in C++

1. Provides a Systematic Way of Storing and Accessing Multiple Values
   Arrays allow you to store multiple values of the same type under a single name, using index-based access. This structure makes data organization and retrieval simple and structured.
2. Offers Better Performance in Memory Handling
   Arrays are stored in contiguous memory blocks, which enhances cache performance and reduces overhead. This improves execution speed during data access and manipulation.
3. Simplifies Code by Grouping Related Variables
   Instead of declaring many separate variables, arrays group them efficiently. This reduces code redundancy and makes programs cleaner and easier to maintain.
4. Enables Iteration and Efficient Searching/Sorting
   Arrays are ideal for looping through elements, making tasks like searching, sorting, and modifying large datasets more efficient using iterative or algorithmic techniques.
5. Supports Static Memory Allocation for Predictable Size
   Since array size is usually defined at compile-time, memory allocation becomes predictable and fixed. This is useful in real-time systems and embedded applications where memory usage must be controlled.
6. Essential for Implementing Matrices, Tables, and Other Structured Data
   Arrays are the foundation for creating 2D and 3D structures like matrices, grids, and tables. They play a key role in mathematical modeling, scientific computing, and data processing tasks.

Applications of Arrays

1. Frequently used in competitive programming for large input sets.
2. Useful in data storage, processing, and manipulation.
3. Foundational structure in algorithms like searching and sorting.
4. Applied in scientific computation and simulation.
5. Integral in graphics programming for pixel and object storage.
6. Used in embedded systems for efficient resource management.