臺大課程網

高等演算法

112-2 開課
  • 流水號

    30365

  • 課號

    EE5182

  • 課程識別碼

    921 U2590

  • 無分班

  • 3 學分
  • 授課對象

    電機工程學研究所

  • 選修
  • 陳和麟 副教授

  • 五 2, 3, 4
  • 明達231

  • 3 類登記

  • 修課總人數 180 人

    本校 180 人

  • 無領域專長

  • 中文授課
  • NTU COOL
  • 備註
  • 本校選課狀況

    載入中...
  • 課程概述
    本校同學希望旁聽課程請直接聯絡助教吳政宏 r11921036@ntu.edu.tw In this course, we will study various techniques for designing and analyzing algorithms. We will mainly focus on problems for which the exact algorithm is not known or not efficient enough and problems with resource constraints. Besides designing efficient algorithms, proving the performance guarantees is also a main topic of this course. In the first half of the semester, we will focus on approximation algorithms. Approximation algorithms are algorithms that find near-optimal solutions with provable performance guarantees in polynomial time. We will cover design techniques such as rounding and primal-dual algorithms. In the second half of the semester, we will focus on different topics in randomized algorithms. We will cover algorithm design techniques such as hashing, sampling, random walks, and derandomization.
  • 課程目標
    本課程主要針對從事演算法研究的學生,提供演算法設計的技巧與未來學習的方向。
  • 課程要求
    預修課程: 演算法、機率、離散數學、資料結構
  • 預期每週課後學習時數
  • Office Hour
    星期三10:00 - 12:00
    星期五12:00 - 13:00
    星期四10:00 - 12:00
    TA office hours: 吳政宏, email: r11921036@ntu.edu.tw, office hour: Wed 10:00-12:00 MD709 李怡萱, email: r12921065@ntu.edu.tw, office hour: Thu 10:00-12:00 MD709
  • 指定閱讀
  • 參考書目
    Approximation Algorithms, Vazirani Design of Approximation Algorithms, Williamson and Shmoys Randomized Algorithms, Motwani and Raghavan
  • 評量方式
    40%
    作業
    約四次手寫作業
    35%
    期中考
    25%
    期末考
  • 針對學生困難提供學生調整方式
    調整方式說明
    上課形式

    以錄影輔助

    其他

    由師生雙方議定

  • 課程進度
    第 0 週*** 進度視教學狀況隨時調整 ***
    2/23第 01 週Course Overview, Knapsack Problem, PTAS/FPTAS
    3/1第 02 週Approximation Algorithms: Subset Sum, Bin Packing
    3/8第 03 週Linear Programming, ILP, LP-Relaxation
    3/15第 04 週Duality
    3/22第 05 週Primal-Dual Algorithms
    3/29第 06 週Greedy and Local Search Algorithms
    4/5第 07 週No class (spring break)
    4/12第 08 週Local Search Algorithms, Homework Solutions
    4/19第 09 週Midterm Exam (covering week 1 - week 8)
    4/26第 10 週Randomized Algorithm, Derandomization
    5/3第 11 週Hashing
    5/10第 12 週Markov Chain, Random Walk
    5/17第 13 週Counting and Sampling
    5/24第 14 週Counting, Homework Solutions
    5/31第 15 週Online Algorithms, Streaming Algorithms (Videos from last year's lectures only)
    6/7第 16 週Final Exam (covering week 10 - week 14)