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Data Party THU
Data Party THU
Aug 30, 2025 · Artificial Intelligence

Understanding Multi‑Armed Bandits: Balancing Exploration and Exploitation in Reinforcement Learning

Multi‑armed bandit models illustrate the core exploration‑exploitation dilemma in reinforcement learning, covering greedy, ε‑greedy, and optimistic‑initial‑value strategies, as well as sample‑average and incremental Q‑value estimation methods with practical examples and visual illustrations.

Q-value estimationexploration vs exploitationgreedy
0 likes · 15 min read
Understanding Multi‑Armed Bandits: Balancing Exploration and Exploitation in Reinforcement Learning
IT Services Circle
IT Services Circle
Aug 17, 2024 · Fundamentals

Algorithmic Interview Problems: Travel Plan, Homework Scheduling, Flower Bed Beauty, and Simple Hash Table Restoration

This article presents four algorithmic interview problems covering travel scheduling with priority constraints, optimal homework task ordering, maximizing distinct garden beauty scores after a single flip operation, and reconstructing insertion sequences for a linear‑probing hash table, each with detailed analysis, solution ideas, and reference Python code.

coding interviewgreedyhash table
0 likes · 15 min read
Algorithmic Interview Problems: Travel Plan, Homework Scheduling, Flower Bed Beauty, and Simple Hash Table Restoration
Model Perspective
Model Perspective
Jan 22, 2024 · Artificial Intelligence

How A/B Testing and the ε‑Greedy Multi‑Armed Bandit Can Boost Decisions

This article explains the principles of A/B testing and the ε‑greedy multi‑armed bandit algorithm, illustrates their practical use in e‑commerce recommendation optimization, and draws broader life lessons about balancing exploration and exploitation for better personal and professional decisions.

A/B testingRecommendation Systemsexploration vs exploitation
0 likes · 6 min read
How A/B Testing and the ε‑Greedy Multi‑Armed Bandit Can Boost Decisions
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Oct 6, 2023 · Interview Experience

Maximum Coloring of a 01 String – DP and Greedy Solutions Explained

Given a binary string, you may color some '1's red and some '0's blue but adjacent opposite bits cannot both be colored; this article presents O(N) dynamic‑programming and greedy algorithms that compute the maximum number of characters that can be colored, with full code examples in Python, Java, and C++.

algorithmbinary stringcoding interview
0 likes · 9 min read
Maximum Coloring of a 01 String – DP and Greedy Solutions Explained
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Aug 30, 2023 · Fundamentals

Greedy Merchant: Maximize Multi‑Product Profit in Limited Days

This article explains a coding problem where a merchant trades multiple goods over several days, describes the input and output formats, demonstrates how each product can be handled independently using a greedy approach identical to LeetCode 122, provides a full Python implementation, and analyzes its time and space complexity.

algorithmcoding interviewdynamic programming
0 likes · 8 min read
Greedy Merchant: Maximize Multi‑Product Profit in Limited Days
IT Architects Alliance
IT Architects Alliance
Jun 15, 2022 · Fundamentals

Mastering Algorithm Strategies: From Greedy to Dynamic Programming

This article provides a comprehensive overview of fourteen algorithmic strategies—including greedy, recurrence, recursion, enumeration, backtracking, divide‑and‑conquer, and dynamic programming—explaining their characteristics, typical use cases, inter‑relationships, and the types of problems each approach best addresses.

AlgorithmsRecursiondivide and conquer
0 likes · 9 min read
Mastering Algorithm Strategies: From Greedy to Dynamic Programming
IT Services Circle
IT Services Circle
Apr 11, 2022 · Fundamentals

Maximum Satisfaction (LeetCode 1402) – Greedy Algorithm Solution

This article explains the LeetCode 1402 problem of maximizing the total satisfaction score by arranging dishes, describes the greedy approach of sorting and using suffix sums, and provides a Java implementation that iteratively adds positive suffix sums to compute the optimal result.

JavaLeetCodeSorting
0 likes · 4 min read
Maximum Satisfaction (LeetCode 1402) – Greedy Algorithm Solution
WeDoctor Frontend Technology
WeDoctor Frontend Technology
Oct 13, 2021 · Fundamentals

Decode Chinese ID Cards & Solve Classic LeetCode Problems with JavaScript

This article explains how to parse Chinese ID numbers to extract gender and age, demonstrates the checksum calculation, and then walks through classic algorithm challenges—including climbing stairs, longest palindrome, trapping rain water, and greedy cookie allocation—providing clear JavaScript implementations for each.

AlgorithmsLeetCodedynamic programming
0 likes · 18 min read
Decode Chinese ID Cards & Solve Classic LeetCode Problems with JavaScript
NiuNiu MaTe
NiuNiu MaTe
Sep 15, 2021 · Fundamentals

How to Maximize Mooncake Profit: Brute Force, Greedy, and DP Solutions

This article presents a classic stock‑like profit maximization problem using mooncake prices, explains the input and expected output, and walks through three solution strategies—brute‑force enumeration, a greedy linear scan, and a dynamic‑programming approach—complete with Go code examples and visual illustrations.

algorithmbrute forcedynamic programming
0 likes · 6 min read
How to Maximize Mooncake Profit: Brute Force, Greedy, and DP Solutions
NiuNiu MaTe
NiuNiu MaTe
Jul 27, 2021 · Fundamentals

Three Clever Ways to Solve the Circular Gas Station Problem

This article explains the classic circular gas‑station challenge, presents its key constraints, walks through a concrete example, and compares three solution strategies—brute‑force, reverse (backtrack) greedy, and a cumulative‑sum graphical method—highlighting their trade‑offs and insights.

algorithmbrute forcecoding interview
0 likes · 7 min read
Three Clever Ways to Solve the Circular Gas Station Problem
Intelligent Backend & Architecture
Intelligent Backend & Architecture
May 19, 2021 · Fundamentals

Master Optimal Algorithms: Recursion, Greedy, Divide-Conquer, DP & Backtracking

This comprehensive guide explores core algorithmic strategies for finding optimal solutions—including recursion, greedy methods, divide-and-conquer, dynamic programming, backtracking, and branch-and-bound—explaining their principles, use-cases, and providing concrete Java and C++ code examples to illustrate implementation details.

AlgorithmsBacktrackingRecursion
0 likes · 54 min read
Master Optimal Algorithms: Recursion, Greedy, Divide-Conquer, DP & Backtracking
NiuNiu MaTe
NiuNiu MaTe
May 11, 2021 · Fundamentals

Can a Frog Jump Across All Platforms? Jump Game Solutions with DFS, DP, Greedy

Given an array where each element indicates the maximum jump length from that position, this article explores multiple strategies—depth‑first search, dynamic programming, greedy, and reverse‑tracking—to determine whether the frog can reach the last board, comparing their efficiency and implementation details.

DFSalgorithmdynamic programming
0 likes · 7 min read
Can a Frog Jump Across All Platforms? Jump Game Solutions with DFS, DP, Greedy
Python Crawling & Data Mining
Python Crawling & Data Mining
Oct 1, 2018 · Fundamentals

Master Python Regex: Unlock $ and ? for Precise String Matching

This article explains Python regular expression special characters such as $ and ?, demonstrates greedy and non‑greedy matching with step‑by‑step examples and screenshots, and shows how to extract substrings correctly using appropriate patterns, helping readers master precise string extraction for web crawling.

Pythongreedynon-greedy
0 likes · 6 min read
Master Python Regex: Unlock $ and ? for Precise String Matching
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Aug 24, 2017 · Fundamentals

Classic Algorithms: Divide‑Conquer, DP, Greedy, Backtracking, Branch‑and‑Bound

This article revisits fundamental algorithmic strategies—divide‑and‑conquer, dynamic programming, greedy methods, backtracking, and branch‑and‑bound—detailing their core ideas, applicable problem characteristics, key considerations, procedural steps, and illustrative examples such as merge sort, coin change, Huffman coding, and shortest‑path problems.

AlgorithmsBacktrackingbranch-and-bound
0 likes · 7 min read
Classic Algorithms: Divide‑Conquer, DP, Greedy, Backtracking, Branch‑and‑Bound
ITPUB
ITPUB
May 2, 2017 · Fundamentals

Master Core Algorithms: Sorting, Graph Traversal, Greedy & Complexity Basics

This guide presents concise explanations of essential algorithms—including quick sort, merge sort, bucket and radix sorts, depth‑first and breadth‑first searches, shortest‑path and minimum‑spanning‑tree methods—along with their stability, time‑complexity analyses, greedy strategies, bit‑manipulation tricks, and asymptotic notation, and points to a GitHub repository for reference implementations.

AlgorithmsInterview PrepSorting
0 likes · 10 min read
Master Core Algorithms: Sorting, Graph Traversal, Greedy & Complexity Basics
ITPUB
ITPUB
Apr 24, 2017 · Fundamentals

Essential Algorithm Cheat Sheet: Sorting, Graph, Greedy, and Bit Operations

This article provides a concise reference of core algorithms—including sorting methods, graph traversals, shortest‑path techniques, greedy strategies, bit‑level manipulations, and asymptotic notation—detailing their stability, time complexities, key concepts, and practical examples for interview preparation.

AlgorithmsInterview PreparationSorting
0 likes · 8 min read
Essential Algorithm Cheat Sheet: Sorting, Graph, Greedy, and Bit Operations
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Dec 17, 2016 · Fundamentals

Understanding Classic Algorithms: Divide‑and‑Conquer, DP, Greedy, Backtracking

The article revisits fundamental algorithmic strategies—including divide‑and‑conquer, dynamic programming, greedy methods, backtracking, and branch‑and‑bound—detailing their core ideas, applicable problem characteristics, key considerations, procedural steps, and illustrative examples such as merge sort, coin change, Huffman coding, minimum spanning trees, and shortest‑path problems.

AlgorithmsBacktrackingbranch-and-bound
0 likes · 7 min read
Understanding Classic Algorithms: Divide‑and‑Conquer, DP, Greedy, Backtracking