Author: Zen and the Art of Computer Programming
1. What is algorithmic complexity?
Complexity is a metric used to evaluate resource consumption such as algorithm execution time, space and memory consumption. The complexity of the algorithm can be used to guide various engineering practices, such as: selecting the optimal algorithm, optimizing its performance, and providing a reasonable resource allocation for the system.
However, the complexity is not static. With the continuous iteration, optimization and update of the algorithm, the complexity is constantly changing. In the past few decades, the main focus of complexity research has been studied from multiple dimensions such as running time, space occupation, time complexity, space complexity, polynomial time complexity, and Θ notation, effectively improving the estimation of algorithm complexity. This article will focus on this research, discuss and summarize the differences and applications of different complexity analysis methods, and provide readers with relevant technical knowledge in combination with actual engineering cases.
2. Why do we need algorithm complexity analysis?
1) Improvement of code readability and robustness. Complexity analysis can help developers understand and master the time, space and memory consumption rules of the algorithm, which is conducive to improving the readability and robustness of the algorithm;
2) It is important for optimizing performance. Algorithm complexity is crucial to optimize algorithm performance. For example, by analyzing the time, space, and memory consumption rules of the algorithm, we can determine which optimization directions may generate the greatest benefits and optimize them accordingly;
3) Reduce the risk of misjudgment. In the implementation stage, developers may misestimate the complexity of the algorithm, resulting in performance gaps or even system failures, while complexity analysis tools can provide accurate data support and reduce the risk of developers' misjudgment. At the same time, complexity analysis can also be used to calculate the implementation difficulty of the actual algorithm, which helps to measure the development workload.