Algorithm - Tag - Zhiverse

Time Complexity

Author — Thu, 01 Jan 2026 11:36:20 +0800

Time complexity is used to describe how the computation time of an algorithm grows as the input size increases during its execution. Because the processing capabilities, hardware architecture, and system environment differ across computer devices, comparing algorithms solely based on actual execution time often fails to yield representative and portable conclusions. Therefore, time complexity is usually measured by the “number of basic operations executed,” and asymptotic complexity is employed to analyze the efficiency of algorithms, establishing a hardware-independent and universally applicable performance evaluation method.

From Gradient Descent to Stochastic Gradient Descent

Author — Sat, 30 Aug 2025 10:54:32 +0800

Cover image was generated by ChatGPT.

Gradient descent is an iterative method used in most modern models to help find optimal parameters. However, in today’s era of big data, the massive amount of data results in significant time costs for gradient descent. One key goal is to compute parameters quickly without sacrificing accuracy.

Bellman-Ford Algorithm

Author — Thu, 17 Jul 2025 15:20:54 +0800

To address the limitation in Dijkstra's Algorithm, which cannot handle paths with **negative** edge weights, the Bellman-Ford Algorithm was developed. The Bellman-Ford Algorithm performs $V - 1$ relaxation operations for $V$ nodes, repeatedly relaxing all edges to continuously improve and find the shortest paths.

K-Means Clustering

Author — Fri, 11 Jul 2025 14:49:59 +0800

K-means clustering, often referred to in Chinese as the "k-means algorithm", is a method used to partition $n$ data points into $k$ clusters. Each point is assigned to the cluster whose center (centroid) is closest to it.

A* Algorithm

Author — Tue, 08 Jul 2025 14:06:30 +0800

About Greedy Algorithm

Author — Mon, 07 Jul 2025 15:38:46 +0800

The **Greedy Algorithm** is an approach that solves problems by **choosing the option that appears most favorable at each step**. It makes simple and intuitive decisions based on the **locally optimal choice** among all available options at the moment, with the expectation that these local choices will accumulate into a globally optimal solution, continuing this process until a termination condition is met.

Dijkstra's Algorithm

Author — Wed, 02 Jul 2025 17:12:30 +0800

Dijkstra's Algorithm is a method for finding the shortest path between nodes in a graph. It was originally designed to find the shortest path between two points, but was later extended to find the shortest paths from a fixed point to all other points, forming what is called a "shortest path tree".

Introduction to Perceptron

Author — Tue, 20 May 2025 22:30:24 +0800

The Perceptron is one of the earliest models of artificial neural networks and laid the foundation for modern machine learning, often regarded as the 'ancestor' of neural networks. It performs simple binary classification tasks and mathematically simulates the logic of how neurons operate. Even today, it is frequently used as a teaching example for beginners in machine learning.