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课时1:Why Study Algorithms
课时2:Integer Multiplication
课时3:Karatsuba Multiplication
课时4:Merge Sort Motivation and Example
课时5:Merge Sort Pseudocode
课时6:Merge Sort Analysis
课时7:Guiding Principles for Analysis of Algorithms
课时8:The Gist
课时9:Big Oh Notation
课时10:Basic Examples
课时11:Big Omega and Theta
课时12:Additional Examples Review Optional
课时13:On log n Algorithm for Counting Inversions I
课时14:On log n Algorithm for Counting Inversions II
课时15:Strassen 's Subcubic Matrix Multiplication Algorithm
课时16:On log n Algorithm for Closest Pair I Advanced Optional
课时17:On log n Algorithm for Closest Pair II Advanced Optional
课时18:Motivation
课时19:Formal Statement
课时20:Examples
课时21:Proof I
课时22:Interpretation of the 3 Cases
课时23:Proof II
课时24:Quicksort Overview
课时25:Partitioning Around a Pivot
课时26:Choosing a Good Pivot
课时27:Analysis I A Decomposition Principle Advanced Optional
课时28:Analysis II The Key Insight Advanced Optional
课时29:Analysis III Final Calculations Advanced Optional
课时30:Omegan log n Lower Bound for Comparison Based Sorting Advanced Optional
课时31:Randomized Selection Algorithm
课时32:Randomized Selection Analysis
课时33:Deterministic Selection Algorithm Advanced Optional
课时34:Deterministic Selection Analysis I Advanced Optional
课时35:Deterministic Selection Analysis II Advanced Optional
课时36:Correctness of Quicksort Review Optional
课时37:Part I Review Optional
课时38:Graphs and Minimum Cuts
课时39:Graph Representations
课时40:Graph Search Overview
课时41:Breadth First Search BFS The Basics
课时42:BFS and Shortest Paths
课时43:BFS and Undirected Connectivity
课时44:Depth First Search DFS The Basics
课时45:Topological Sort
课时46:Computing Strong Components The Algorithm
课时47:Computing Strong Components The Analysis
课时48:Structure of the Web Optional
课时49:Dijkstra 's Shortest Path Algorithm
课时50:Dijkstra 's Algorithm Examples
课时51:Correctness of Dijkstra 's Algorithm Advanced Optional
课时52:Dijkstra 's Algorithm Implementation and Running Time
课时53:Data Structures Overview
课时54:Heaps Operations and Applications
课时55:Heaps Implementation Details Advanced Optional
课时56:Balanced Search Trees Operations and Applications
课时57:Binary Search Tree Basics, Part I
课时58:Binary Search Tree Basics, Part II
课时59:Rotations Advanced Optional
课时60:Hash Tables Operations and Applications
课时61:Hash Tables Implementation Details, Part I
课时62:Hash Tables Implementation Details, Part II
课时63:Pathological Data Sets and Universal Hashing Motivation
课时64:Bloom Filters The Basics
课时65:Bloom Filters Heuristic Analysis
课时66:INTRODUCTION TO GREEDY ALGORITHMS- Greedy Algorithms
课时67:A SCHEDULING APPLICATION- Problem Definition
课时68:A SCHEDULING APPLICATION- Greedy Algorithm
课时69:A SCHEDULING APPLICATION- Correctness Proof - Part I
课时70:A SCHEDULING APPLICATION- Correctness Proof - Part II
课时71:A SCHEDULING APPLICATION- Handling Ties
课时72:HUFFMAN CODES- Introduction and Motivation
课时73:HUFFMAN CODES- Problem Definition
课时74:HUFFMAN CODES- A Greedy Algorithm
课时75:HUFFMAN CODES- A More Complex Example
课时76:HUFFMAN CODES- Correctness Proof 1
课时77:HUFFMAN CODES- Correctness Proof 2
课时78:PRIM'S MINIMUM SPANNING TREE ALGORITHM- MST Problem Definition
课时79:PRIM'S MINIMUM SPANNING TREE ALGORITHM- Prims MST Algorithm
课时80:PRIM'S MINIMUM SPANNING TREE ALGORITHM- Fast Implementation 1
课时81:PRIM'S MINIMUM SPANNING TREE ALGORITHM- Fast Implementation 2
课时82:PRIM'S MINIMUM SPANNING TREE ALGORITHM- Correctness Proof 1
课时83:PRIM'S MINIMUM SPANNING TREE ALGORITHM- Correctness Proof 2
课时84:KRUSKAL'S MINIMUM SPANNING TREE ALGORITHM- Kruskals MST Algorithm
课时85:KRUSKAL'S MINIMUM SPANNING TREE ALGORITHM- Implementing Kruskals Algorithm via Union Find
课时86:KRUSKAL'S MINIMUM SPANNING TREE ALGORITHM- Implementing Kruskals Algorithm via Union Find 2
课时87:ADVANCED UNION-FIND- Lazy Unions
课时88:KRUSKAL'S MINIMUM SPANNING TREE ALGORITHM- Correctness of Kruskals Algorithm
课时89:CLUSTERING- Application to Clustering
课时90:INTRODUCTION TO DYNAMIC PROGRAMMING- Introduction - Weighted Independent Sets in Path Graphs
课时91:INTRODUCTION TO DYNAMIC PROGRAMMING- WIS in Path Graphs - Optimal Substructure
课时92:INTRODUCTION TO DYNAMIC PROGRAMMING- WIS in Path Graphs - A Linear Time Algorithm
课时93:INTRODUCTION TO DYNAMIC PROGRAMMING- WIS in Path Graphs - A Reconstruction Algorithm
课时94:INTRODUCTION TO DYNAMIC PROGRAMMING- Principles of Dynamic Programming
课时95:THE KNAPSACK PROBLEM- The Knapsack Problem
课时96:THE KNAPSACK PROBLEM- A Dynamic Programming Algorithm
课时97:THE KNAPSACK PROBLEM- Example
课时98:SEQUENCE ALIGNMENT- Optimal Substructure
课时99:SEQUENCE ALIGNMENT- A Dynamic Programming Algorithm
课时100:OPTIMAL BINARY SEARCH TREES- Problem Definition
课时101:OPTIMAL BINARY SEARCH TREES- Optimal Substructure
课时102:OPTIMAL BINARY SEARCH TREES- Proof of Optimal Substructure
课时103:OPTIMAL BINARY SEARCH TREES- A Dynamic Programming Algorithm 1
课时104:OPTIMAL BINARY SEARCH TREES- A Dynamic Programming Algorithm 2
课时105:THE BELLMAN-FORD ALGORITHM- Single-Source Shortest Paths, Revisited
课时106:THE BELLMAN-FORD ALGORITHM- Optimal Substructure
课时107:THE BELLMAN-FORD ALGORITHM- The Basic Algorithm 1
课时108:THE BELLMAN-FORD ALGORITHM- The Basic Algorithm 2
课时109:THE BELLMAN-FORD ALGORITHM- Detecting Negative Cycles
课时110:ALL-PAIRS SHORTEST PATHS- Problem Definition
课时111:ALL-PAIRS SHORTEST PATHS- Optimal Substructure
课时112:ALL-PAIRS SHORTEST PATHS- The Floyd Warshall Algorithm
课时113:A Field Guide to Algorithm Design (Epilogue to the Algorithms Illuminated book series)
课时114:Overview and Prerequisites
课时115:The Algorithmic Mystery of MST vs. TSP
课时116:Possible Levels of Expertise
课时117:Easy and Hard Problems
课时118:Algorithmic Strategies for NP-Hard Problems
课时119:A Simple Recipe for Proving NP-Hardness
课时120:Rookie Mistakes
课时121:Makespan Minimization Part 1
课时122:Makespan Minimization Part 2
课时123:A Greedy Heuristic for Maximum Coverage Part 1
课时124:A Greedy Heuristic for Maximum Coverage Part 2
课时125:A Greedy Heuristic for Influence Maximization 1
课时126:A Greedy Heuristic for Influence Maximization 2
课时127:The 2-OPT Heuristic for the TSP Part 1
课时128:The 2-OPT Heuristic for the TSP Part 2
课时129:Principles of Local Search Part 1
课时130:Principles of Local Search Part 2
课时131:The Bellman-Held-Karp Algorithm for TSP Part 1
课时132:The Bellman-Held-Karp Algorithm for TSP Part 2
课时133:Color Coding Part 1
课时134:Color Coding Part 2
课时135:Problem-Specific Algorithms vs. Magic Boxes
课时136:Mixed Integer Programming Solvers
课时137:Satisfiability Solvers
课时138:Reductions Revisited
课时139:SAT and the Cook-Levin Theorem
课时140:The Big Picture
课时141:Independent Set Is NP-Hard
课时142:Directed Hamiltonian Path Is NP-Hard
课时143:The TSP Is NP-Hard
课时144:Subset Sum Is NP-Hard
课时145:Amassing Evidence of Intractability
课时146:Decision, Search, and Optimization
课时147:NP- Problems with Easily Recognized Solutions
课时148:The P!=NP Conjecture
课时149:The Exponential Time Hypothesis
课时150:NP-Completeness
课时151:Repurposing Wireless Spectrum
课时152:Greedy Heuristics for Buying Back Licenses Pt 1
课时153:Greedy Heuristics for Buying Back Licenses Pt 2
课时154:Feasibility Checking Pt 1
课时155:Feasibility Checking Pt 2
课时156:Implementation as a Descending Clock Auction
课时157:The Final Outcome
课时158:A Field Guide to Algorithm Design (Epilogue to the Algorithms Illuminated book series)
课程介绍共计158课时,1天14小时39分27秒
算法是计算机科学的核心与灵魂。算法的应用范围极广,网络路由、计算基因组学、公钥加密学和数据库系统等的实现都需要算法。研究算法可以帮助我们成为更优秀的程序员,可以让我们具有更缜密的思维,并成功应对各种场合的技术面试。
这是一本非常容易上手的算法入门图书,它可作为程序员的学习用书,也适合想要学习算法和想提升算法思维能力的读者阅读。
本书主要包括以下内容:
渐进性分析;
大O表示法;
主方法;
快速分治算法;
随机化算法;
排序算法;
选择算法。
算法是计算机科学的核心与灵魂。算法的应用范围极广,网络路由、计算基因组学、公钥加密学和数据库系统等的实现都需要算法。研究算法可以帮助我们成为更优秀的程序员,可以让我们具有更缜密的思维,并成功应对各种场合的技术面试。
这是一本非常容易上手的算法入门图书,它可作为程序员的学习用书,也适合想要学习算法和想提升算法思维能力的读者阅读。
本书主要包括以下内容:
图的搜索和应用;
散列表;
最短路径算法;
布隆过滤器;
随机化算法;
堆;
搜索树。