Memory management is crucial in data structures and algorithms to ensure efficient utilization of resources and optimize performance. Several techniques are commonly used:

1. Static Memory Allocation: Memory is allocated at compile-time and remains fixed throughout the program's execution. Arrays are an example of static memory allocation. While efficient in terms of memory overhead, it lacks flexibility and can lead to wastage if the allocated memory is not fully utilized.
2. Dynamic Memory Allocation: Memory is allocated at runtime, allowing for flexibility in memory usage. In languages like C/C++, this is typically done using functions like malloc() and free(), or new and delete. Dynamic memory allocation allows for efficient memory usage but requires careful management to avoid memory leaks and fragmentation.
3. Stack Allocation: Memory is allocated on the program's stack. It's fast and efficient but limited in size and scope (typically local variables and function call frames). Data structures like arrays and simple variables are often stack-allocated.
4. Heap Allocation: Memory is allocated on the heap, providing more flexibility and space compared to the stack. However, heap allocation can be slower due to the need for dynamic memory management. Complex data structures like trees and linked lists are often heap-allocated.
5. Memory Pooling: Pre-allocating a fixed-size block of memory and managing it as a pool of memory chunks. This can reduce overhead from frequent allocation and deallocation operations.
6. Garbage Collection: Used in languages like Java and C#, where the runtime automatically deallocates memory that is no longer in use. This relieves the programmer from manual memory management but can introduce overhead and unpredictability in performance.
7. Memory Reuse: Recycling memory blocks that are no longer in use rather than freeing them immediately, reducing the overhead of frequent allocation and deallocation.
8. Memory Fragmentation Mitigation: Techniques like compaction (reorganizing memory to reduce fragmentation) and memory pooling can help mitigate fragmentation issues, where available memory is fragmented into small unusable chunks.
9. Smart Pointers: In languages like C++11 and above, smart pointers (e.g., std::shared_ptr, std::unique_ptr) automate memory management by automatically deallocating memory when it's no longer referenced, reducing the risk of memory leaks.
10. Memory Alignment: Ensuring that data structures are aligned to memory boundaries can improve performance by reducing memory access overhead, especially on architectures where unaligned memory access is costly.

Each technique has its advantages and trade-offs, and the choice depends on factors like performance requirements, programming language, and the specific characteristics of the application.