In this online course we’ll implement (in Python) together efficient programs for a problem needed by delivery companies all over the world millions times per day — the travelling salesman problem. The goal in this problem is to visit all the given places as quickly as possible. How to find an optimal solution to this problem quickly? We still don’t have provably efficient algorithms for this difficult computational problem and this is the essence of the P versus NP problem, the most important open question in Computer Science. Still, we’ll implement several solutions for real world instances of the travelling salesman problem.
제공자:
Delivery Problem
캘리포니아 샌디에고 대학교이 강좌에 대하여
최근 조회 2,286회
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
초급 단계
완료하는 데 약 13시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 이탈리아어, 베트남어, 독일어, 러시아어, 영어, 스페인어
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
초급 단계
완료하는 데 약 13시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 이탈리아어, 베트남어, 독일어, 러시아어, 영어, 스페인어
제공자:
캘리포니아 샌디에고 대학교
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
강의 계획표 - 이 강좌에서 배울 내용
완료하는 데 5시간 필요
Traveling Salesman Problem
We start this module with the definition of mathematical model of the delivery problem — the classical traveling salesman problem (usually abbreviated as TSP). We'll then review just a few of its many applications: from straightforward ones (delivering goods, planning a trip) to less obvious ones (data storage and compression, genome assembly). After that, we will together take the first steps in implementing programs for TSP.
완료하는 데 5시간 필요
4개 동영상 (총 43분), 1 개의 읽기 자료, 5 개의 테스트
완료하는 데 4시간 필요
Exact Algorithms
We'll see two general techniques applied to the traveling salesman problem. The first one, branch and bound, is a classical approach in combinatorial optimization that is used for various problems. It can be seen as an improvement of the brute force search: we try to construct a permutation piece by piece, but at each step we check whether it still makes sense to continue constructing the permutation (if it doesn't, we just cut off the current branch). The second one, dynamic programming, is arguably the most popular algorithmic technique. It solves a problem by going through a collection of smaller subproblems.
완료하는 데 4시간 필요
4개 동영상 (총 49분)
완료하는 데 4시간 필요
Approximation Algorithms
As we've seen in the previous modules, solving the traveling salesman problem exactly is hard. In fact, we don't even expect an efficient solution in the nearest future. For this reason, it makes sense to ask: is it possible to find efficiently a solution that is probably suboptimal, but at the same time is close to optimal? It turns out that the answer is yes! We'll learn two algorithms. The first one guarantees to find quickly a solution which is at most twice longer than the optimal one. The second algorithms does not have such guarantees, but it is known to work pretty well in practice.
완료하는 데 4시간 필요
2개 동영상 (총 20분)
검토
- 5 stars76.75%
- 4 stars17.92%
- 3 stars2.80%
- 2 stars2.24%
- 1 star0.28%
DELIVERY PROBLEM의 최상위 리뷰
DG 제공2019년 5월 20일
This course is to the point and challenges you with practical application.
LF 제공2020년 1월 13일
This is a very nice course. I feel that a further explanation in the coding problems would be useful since sometimes you are not sure what one should return from the function.
AS 제공2018년 7월 24일
This final course in 5 course specialization is relatively easy one, although the last problem takes little bit time to solve. Provides good introduction to difficult to learn Delivery problem.
LA 제공2020년 10월 30일
VERY GOOD COURSE IT IS SO BENTIFITIAL TO THE PEOLPLE WHO ARE INTERTESTED TO DEVELOP THE MATHEMATICAL SKILLS
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
이 수료증을 구매하면 무엇을 이용할 수 있나요?
재정 지원을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
