校際選修

115-1 選課時程

進行中

  • 初選第一階段 6/15/2026
  • 初選第二階段 6/22/2026
  • 校際選修 8/24/2026
  • 初選第三階段 8/31/2026
  • 開學後加退選 9/7/2026
  • 逾期加退選 9/21/2026
選課資源

近似演算法(英文授課)

Introduction to Approximation Algorithms

學期
110-1
學分
3 學分
當期課號
5981
永久課號
IOC5222
開課單位
資訊科學與工程研究所
授課教師
高孟駿
校區
光復
類別
選修
上課時間表
週五
6
14:20–15:10
近似演算法(英文授課)
ED102
3 節連堂
7
15:30–16:20
8
16:30–17:20

* 根據陽明交大上課時間表所列

概述

Course website: https://sites.google.com/nycu.edu.tw/5981-i2-approx-algo In this course, we aim to examine approximation algorithms for various categories of fundamental NP-hard problems and learn standard techniques for designing and analyzing approximation algorithms. The problems to be addressed in the lectures include the following: • Cover Problems - Vertex Cover / Dominating Set / Set Cover, Submodular Cover • Location / Clustering Problems - k-Center, k-Median, Facility Location • Packing / Scheduling Problems - Knapsack, Bin Packing, Unrelated / Identical Machine Scheduling • Flow / Cut / Routing Problems - Max Cut, Multiway Cut, Multi-Cut / Multi-Commodity Flow, Sparest Cut • Network Design Problems - Steiner Tree / Forest, Steiner Network Problem (Survival Network Design) • Tour Problems - Traveling Salesman Problem (TSP) We will introduce classic approximation algorithms and state-of-the-art status for the above categories of problems. The lectures will be mostly technique-oriented with the algorithms being examples. Throughout this course, students will learn to design and analyze their own approximation algorithms.

先修科目

Linear Algebra, Algorithms

教學方式

Lectures

評分方式

Homework: 40% Midterm: 30% Paper Reading & Group Presentation: 40%

週次計畫
週次主題
第 1 週Introduction on Approximation Algorithms and Problems to Address
第 2 週Combinatorial-based Methods, O(log n)-approx for Set Cover, 2-approx for k-Center, 3-approx for Facility Location
第 3 週FPTAS for Knapsack, Asymptotic PTAS for Bin Packing, PTAS for Identical Machine Scheduling
第 4 週3/2-approx for Metric TSP, PTAS for Euclidean TSP
第 5 週O(log n)-approx of Metrics by Tree Metrics, Spanners*
第 6 週LP-Based Methods, LP Relaxation as a Tool for Lower-bounding OPT, Design of LP relaxations, LP solvers
第 7 週LP duality: theorem and further properties, Deterministic / Randomized Rounding, 2-approx for Vertex Cover, (1+2/e)-approx for Facility Location, 3/2-approx for Multiway Cut
第 8 週Extreme Point Analysis of the Polytope, Half-Integrality of Vertex Cover, 2-approx for Unrelated Machine Scheduling
第 9 週Dual-Fitting and Primal-Dual Schema, Reinterpretation of a Few Greedy Algorithms, 2-approx for Vertex Cover, 2-approx for Steiner Forest Problem
第 10 週Iterative Rounding, 2-approx for Steiner Network, 2-approx for Capacitated Vertex Cover
第 11 週Iterative Rounding, 2-approx for Steiner Network, 2-approx for Capacitated Vertex Cover
第 12 週Factor-Revealing LP for Combinatorial-Based Methods, A 1.61-approx for Facility Location
第 13 週MISC*, O(log k)-approx for Multi-Cut
第 14 週2-approx for Prize-Collecting Steiner Tree
第 15 週Semi-definite Programming, 0.87856-approx for Max-Cut
第 16 週Hardness of Approximation (if time permits)
第 17 週
第 18 週
教科書

1. Approximation Algorithms, by Vijay Vazirani, Springer-Verlag, 2004. 2. The Design of Approximation Algorithms, by David Williamson and David Shmoys, Cambridge, 2012.

Office Hours
時間
By appointment
聯絡方式
mjkao@nycu.edu.tw