Limited memory refers to a type of computational memory that retains a fixed amount of recent information. It’s commonly used in machine learning models to store a subset of data for making predictions or decisions. Unlike full memory systems, limited memory doesn’t store all historical data but focuses on relevant examples to conserve resources. Algorithms like the Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization method and memory-efficient neural network architectures use this concept to handle large datasets efficiently. Limited memory enables practical applications in real-time prediction, optimization, and tasks where storing all past data is impractical or unnecessary.

1. Data Selection: Choosing a subset of recent data or relevant examples from a larger dataset.

2. Memory Allocation: Allocating a fixed amount of memory resources to store the selected data.

3. Data Retention: Storing the chosen data for a limited period, discarding older entries.

4. Updating Memory: Replacing or updating stored data with new incoming information.

5. Usage in Algorithms: Integrating limited memory concepts into algorithms for optimization, prediction, or decision-making.

6. Data Sampling: Employing sampling techniques to select representative data for memory storage.

7. Feature Extraction: Extracting important features from data to be stored in limited memory.

8. Dynamic Adjustment: Adapting the size of limited memory based on the changing requirements of the task.

9. Efficient Retrieval: Ensuring quick access to stored data for computational tasks.

10. Contextual Relevance: Considering the context of data to determine its relevance for memory storage.

11. Optimization Strategies: Using limited memory efficiently in optimization algorithms like L-BFGS.

12. Algorithmic Design: Incorporating limited memory constraints into the design of memory-efficient algorithms.

13. Resource Management: Balancing memory usage with computational efficiency and accuracy.