The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).
Este curso forma parte de Programa Especializado - Algorithms
Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
ofrecido por
Acerca de este Curso
4.8
378 calificaciones
Habilidades que obtendrás
Data StructureAlgorithmsNp-CompletenessDynamic Programming
Programa - Qué aprenderás en este curso
Semana
1Week 1
The Bellman-Ford algorithm; all-pairs shortest paths.
14 videos (Total 151 minutos), 4 readings, 2 quizzes
14 videos
Optimal Substructure10m
The Basic Algorithm I8m
The Basic Algorithm II10m
Detecting Negative Cycles9m
A Space Optimization12m
Internet Routing I [Optional]11m
Internet Routing II [Optional]6m
Problem Definition7m
Optimal Substructure12m
The Floyd-Warshall Algorithm13m
A Reweighting Technique14m
Johnson's Algorithm I11m
Johnson's Algorithm II11m
4 lecturas
Week 1 Overview10m
Overview, Resources, and Policies10m
Lecture Slides10m
Optional Theory Problems (Week 1)10m
2 ejercicios de práctica
Problem Set #110m
Programming Assignment #12m
Semana
2Week 2
NP-complete problems and exact algorithms for them.
11 videos (Total 122 minutos), 2 readings, 2 quizzes
11 videos
Reductions and Completeness13m
Definition and Interpretation of NP-Completeness I10m
Definition and Interpretation of NP-Completeness II7m
The P vs. NP Question9m
Algorithmic Approaches to NP-Complete Problems12m
The Vertex Cover Problem8m
Smarter Search for Vertex Cover I9m
Smarter Search for Vertex Cover II7m
The Traveling Salesman Problem14m
A Dynamic Programming Algorithm for TSP12m
2 lecturas
Week 2 Overview10m
Optional Theory Problems (Week 2)10m
2 ejercicios de práctica
Problem Set #210m
Programming Assignment #22m
Semana
3Week 3
Approximation algorithms for NP-complete problems.
6 videos (Total 68 minutos), 1 reading, 2 quizzes
6 videos
Analysis of a Greedy Knapsack Heuristic I7m
Analysis of a Greedy Knapsack Heuristic II9m
A Dynamic Programming Heuristic for Knapsack11m
Knapsack via Dynamic Programming, Revisited10m
Ananysis of Dynamic Programming Heuristic15m
1 lectura
Week 3 Overview10m
2 ejercicios de práctica
Problem Set #310m
Programming Assignment #32m
Semana
4Week 4
Local search algorithms for NP-complete problems; the wider world of algorithms.
11 videos (Total 124 minutos), 3 readings, 3 quizzes
11 videos
The Maximum Cut Problem II9m
Principles of Local Search I8m
Principles of Local Search II10m
The 2-SAT Problem14m
Random Walks on a Line16m
Analysis of Papadimitriou's Algorithm14m
Stable Matching [Optional]15m
Matchings, Flows, and Braess's Paradox [Optional]13m
Linear Programming and Beyond [Optional]11m
Epilogue1m
3 lecturas
Week 4 Overview10m
Optional Theory Problems (Week 4)10m
Info and FAQ for final exam10m
3 ejercicios de práctica
Problem Set #410m
Programming Assignment #42m
Final Exam20m
71%
comenzó una nueva carrera después de completar estos cursos
67%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Principales revisiones
por AA•Feb 28th 2017
This challenging course improves understanding of algorithms and is intellectually stimulating. I learnt the theory behind algorithms and how they are applied to solve real world problems.
por AS•Aug 23rd 2018
This is the most challenging course in this specialization. Assignments as well as test questions require good amount of thinking.\n\nOne of the best courses I did on Coursera.
Instructor
Tim Roughgarden
ProfessorComputer Science
Acerca de Stanford University
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
Acerca del programa especializado Algorithms
Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.
About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.
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