Informações sobre o curso: 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. While designing these solutions, we will rely heavily on the material learned in the courses of the specialization: proof techniques, combinatorics, probability, graph theory. We’ll see several examples of using discrete mathematics ideas to get more and more efficient solutions.
Para quem é direcionado este curso: Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
Desenvolvido por: University of California San Diego, National Research University Higher School of Economics
Ministrado por: Alexander S. Kulikov, Visiting Professor
Department of Computer Science and Engineering
| Informações básicas | |
| Nível | Básico |
| Compromisso | 3 weeks of study, 2–5 hours/week |
| Idioma | English |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
Programa
SEMANA 1
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.
4 videos, 1 leituras
- Vídeo: Shortest Common Superstring Problem
- Reading: Additional Materials
- Vídeo: Brute Force Search
- Vídeo: Nearest Neighbor
- Notebook: Draw Hamiltonian cycles
- Notebook: Average Weight: Examples
Nota atribuída: Cycle Weight
Nota atribuída: Brute Force Algorithm
Nota atribuída: Average Weight
Nota atribuída: Nearest Neighbors
SEMANA 2
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.
4 videos, 1 teste para praticar
- Vídeo: Dynamic Programming: Main Ideas
- Vídeo: Dynamic Programming: Representing Subsets
- Vídeo: Dynamic Programming: Code
- Quiz pour s'exercer: Dynamic Programming
- Notebook: Integer Linear Programming (Optional)
Nota atribuída: Branch and Bound
SEMANA 3
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.
2 videos
- Vídeo: Local Search
- Notebook: 2-Approximation. Examples.
Nota atribuída: 2-Approximation
Perguntas frequentes
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Desenvolvedores
University of California San Diego
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.
National Research University Higher School of Economics
National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more.
Learn more on www.hse.ru
Custo
| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
Classificações e avaliações
Great way to end a really detailed and engaging specialization that introduces anyone with a minimum background in Python to Algorithms.
perfect course! very easy and interesting to follow. Pseudo-Algorithms were very useful and helped a lot to understand the concepts.
Good exercises!
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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.