Este curso faz parte do Programa de cursos integrados Estruturas de dados e algoritmos

oferecido por

Universidade da Califórnia, San Diego

National Research University Higher School of Economics

Programa de cursos integrados Estruturas de dados e algoritmos

Universidade da Califórnia, San Diego

Informações sobre o curso

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You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

Sugerido: 4 weeks of study, 4-8 hours/week...

Legendas: Inglês

Python ProgrammingLinear Programming (LP)Np-CompletenessDynamic Programming

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

Sugerido: 4 weeks of study, 4-8 hours/week...

Legendas: Inglês

Semana

1Network flows show up in many real world situations in which a good needs to be transported across a network with limited capacity. You can see it when shipping goods across highways and routing packets across the internet. In this unit, we will discuss the mathematical underpinnings of network flows and some important flow algorithms. We will also give some surprising examples on seemingly unrelated problems that can be solved with our knowledge of network flows....

9 vídeos (total de (Total 72 mín.) min), 3 leituras, 2 testes

Introduction3min

Network Flows9min

Residual Networks10min

Maxflow-Mincut7min

The Ford–Fulkerson Algorithm7min

Slow Example3min

The Edmonds–Karp Algorithm11min

Bipartite Matching11min

Image Segmentation7min

Slides and Resources on Flows in Networks10min

Available Programming Languages10min

FAQ on Programming Assignments10min

Flow Algorithms10min

Semana

2Linear programming is a very powerful algorithmic tool. Essentially, a linear programming problem asks you to optimize a linear function of real variables constrained by some system of linear inequalities. This is an extremely versatile framework that immediately generalizes flow problems, but can also be used to discuss a wide variety of other problems from optimizing production procedures to finding the cheapest way to attain a healthy diet. Surprisingly, this very general framework admits efficient algorithms. In this unit, we will discuss some of the importance of linear programming problems along with some of the tools used to solve them....

10 vídeos (total de (Total 84 mín.) min), 1 leitura, 2 testes

Introduction5min

Linear Programming8min

Linear Algebra: Method of Substitution5min

Linear Algebra: Gaussian Elimination10min

Convexity9min

Duality12min

(Optional) Duality Proofs7min

Linear Programming Formulations8min

The Simplex Algorithm10min

(Optional) The Ellipsoid Algorithm6min

Slides and Resources on Linear Programming10min

Linear Programming Quiz10min

Semana

3Although many of the algorithms you've learned so far are applied in practice a lot, it turns out that the world is dominated by real-world problems without a known provably efficient algorithm. Many of these problems can be reduced to one of the classical problems called NP-complete problems which either cannot be solved by a polynomial algorithm or solving any one of them would win you a million dollars (see Millenium Prize Problems) and eternal worldwide fame for solving the main problem of computer science called P vs NP. It's good to know this before trying to solve a problem before the tomorrow's deadline :) Although these problems are very unlikely to be solvable efficiently in the nearest future, people always come up with various workarounds. In this module you will study the classical NP-complete problems and the reductions between them. You will also practice solving large instances of some of these problems despite their hardness using very efficient specialized software based on tons of research in the area of NP-complete problems....

16 vídeos (total de (Total 115 mín.) min), 2 leituras, 2 testes

Search Problems9min

Traveling Salesman Problem7min

Hamiltonian Cycle Problem8min

Longest Path Problem1min

Integer Linear Programming Problem3min

Independent Set Problem3min

P and NP4min

Reductions5min

Showing NP-completeness6min

Independent Set to Vertex Cover5min

3-SAT to Independent Set14min

SAT to 3-SAT7min

Circuit SAT to SAT12min

All of NP to Circuit SAT5min

Using SAT-solvers14min

Slides and Resources on NP-complete Problems10min

Minisat Installation Guide10min

NP-complete Problems12min

Semana

4After the previous module you might be sad: you've just went through 5 courses in Algorithms only to learn that they are not suitable for most real-world problems. However, don't give up yet! People are creative, and they need to solve these problems anyway, so in practice there are often ways to cope with an NP-complete problem at hand. We first show that some special cases on NP-complete problems can, in fact, be solved in polynomial time. We then consider exact algorithms that find a solution much faster than the brute force algorithm. We conclude with approximation algorithms that work in polynomial time and find a solution that is close to being optimal. ...

11 vídeos (total de (Total 119 mín.) min), 1 leitura, 2 testes

Introduction4min

2-SAT10min

2-SAT: Algorithm12min

Independent Sets in Trees14min

3-SAT: Backtracking11min

3-SAT: Local Search12min

TSP: Dynamic Programming15min

TSP: Branch and Bound9min

Vertex Cover9min

Metric TSP12min

TSP: Local Search6min

Slides and Resources on Coping with NP-completeness10min

Coping with NP-completeness6min

comecei uma nova carreira após concluir estes cursos

consegui um benefício significativo de carreira com este curso

recebi um aumento ou promoção

por EM•Jan 4th 2018

As usual, complex arguments explained in simple terms!\n\nSome problems are really tough! (e.g. there's a problem from Google Code Jam).\n\nThank you for this course!

por NN•Jan 4th 2017

Loved what I learnt, I also implemented a project using Google MAP API for the organization I'm working at

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 (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 communicamathematics, engineering, and more.
Learn more on www.hse.ru...

This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine....

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