Optimización discreta

4.8
estrellas
606 calificaciones
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138 reseña

Professor Pascal Van Hentenryck +1 instructores más

52.877 ya inscrito

ofrecido por

Optimización discreta

Universidad de Melbourne

Acerca de este Curso

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Tired of solving Sudokus by hand? This class teaches you how to solve complex search problems with discrete optimization concepts and algorithms, including constraint programming, local search, and mixed-integer programming.
Optimization technology is ubiquitous in our society. It schedules planes and their crews, coordinates the production of steel, and organizes the transportation of iron ore from the mines to the ports. Optimization clears the day-ahead and real-time markets to deliver electricity to millions of people. It organizes kidney exchanges and cancer treatments and helps scientists understand the fundamental fabric of life, control complex chemical reactions, and design drugs that may benefit billions of individuals.

This class is an introduction to discrete optimization and exposes students to some of the most fundamental concepts and algorithms in the field. It covers constraint programming, local search, and mixed-integer programming from their foundations to their applications for complex practical problems in areas such as scheduling, vehicle routing, supply-chain optimization, and resource allocation.

Resultados profesionales del estudiante

29%

comenzó una nueva carrera después de completar estos cursos

17%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

Fechas límite flexibles

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Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

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Nivel intermedio

Aprox. 65 horas para completar

Inglés (English)

Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)

Habilidades que obtendrás

Constraint ProgrammingBranch And BoundDiscrete OptimizationLinear Programming (LP)

Resultados profesionales del estudiante

29%

comenzó una nueva carrera después de completar estos cursos

17%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Nivel intermedio

Aprox. 65 horas para completar

Inglés (English)

Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)

Instructores

Calificación del instructor

4,83/5 (96 calificaciones)

Professor Pascal Van Hentenryck

52.877 alumnos

1 curso

Dr. Carleton Coffrin

Adjunct Lecturer

Computing and Information Systems

54.706 alumnos

2 cursos

ofrecido por

Universidad de Melbourne

The University of Melbourne is an internationally recognised research intensive University with a strong tradition of excellence in teaching, research, and community engagement. Established in 1853, it is Australia's second oldest University.

Programa - Qué aprenderás en este curso

Calificación del contenido96%(3,969 calificaciones)

Semana

Semana 1

2 horas para completar

Welcome

These lectures and readings give you an introduction to this course: its philosophy, organization, and load. They also tell you how the assignments are a significant part of the class. This week covers the common input/output organization of the assignments, how they are graded, and how to succeed in this class.

2 horas para completar

4 videos (Total 43 minutos), 2 lecturas, 1 cuestionario

Semana

Semana 2

7 horas para completar

Knapsack

These lectures introduce optimization problems and some optimization techniques through the knapsack problem, one of the most well-known problem in the field. It discusses how to formalize and model optimization problems using knapsack as an example. It then reviews how to apply dynamic programming and branch and bound to the knapsack problem, providing intuition behind these two fundamental optimization techniques. The concept of relaxation and search are also discussed.

7 horas para completar

9 videos (Total 101 minutos)

Semana

Semana 3

17 horas para completar

Constraint Programming

Constraint programming is an optimization technique that emerged from the field of artificial intelligence. It is characterized by two key ideas: To express the optimization problem at a high level to reveal its structure and to use constraints to reduce the search space by removing, from the variable domains, values that cannot appear in solutions. These lectures cover constraint programming in detail, describing the language of constraint programming, its underlying computational paradigm and how it can be applied in practice.

17 horas para completar

13 videos (Total 248 minutos), 1 lectura, 2 cuestionarios

13 videos

CP 1 - intuition, computational paradigm, map coloring, n-queens27m

CP 2 - propagation, arithmetic constraints, send+more=money26m

CP 3 - reification, element constraint, magic series, stable marriage16m

CP 4 - global constraint intuition, table constraint, sudoku19m

CP 5 - symmetry breaking, BIBD, scene allocation18m

CP 6 - redundant constraints, magic series, market split11m

CP 7 - car sequencing, dual modeling18m

CP 8 - global constraints in detail, knapsack, alldifferent33m

CP 9 - search, first-fail, euler knight, ESDD25m

CP 10 - value/variable labeling, domain splitting, symmetry breaking in search28m

Graph Coloring6m

Optimization Tools5m

Set Cover8m

1 lectura

Optimization Tools10m

Semana

Semana 4

13 horas para completar

Local Search

Local search is probably the oldest and most intuitive optimization technique. It consists in starting from a solution and improving it by performing (typically) local perturbations (often called moves). Local search has evolved substantially in the last decades with a lot of attention being devoted on which moves to explore. These lectures explore the theory and practice of local search, from the concept of neighborhood and connectivity to meta-heuristics such as tabu search and simulated annealing.

13 horas para completar

10 videos (Total 191 minutos)

10 videos

LS 1 - intuition, n-queens13m

LS 2 - swap neighborhood, car sequencing, magic square15m

LS 3 - optimization, warehouse location, traveling salesman, 2-opt, k-opt23m

LS 4 - optimality vs feasibility, graph coloring22m

LS 5 - complex neighborhoods, sports scheduling21m

LS 6 - escaping local minima, connectivity15m

LS 7 - formalization, heuristics, meta-heuristics introduction22m

LS 8 - iterated location search, metropolis heuristic, simulated annealing, tabu search intuition18m

LS 9 - tabu search formalized, aspiration, car sequencing, n-queens26m

Traveling Salesman10m

Semana

Semana 5

2 horas para completar

Linear Programming

Linear programming has been, and remains, a workhorse of optimization. It consists in optimizing a linear objective subject to linear constraints, admits efficient algorithmic solutions, and is often an important building block for other optimization techniques. These lectures review fundamental concepts in linear programming, including the infamous simplex algorithm, simplex tableau, and duality. .

2 horas para completar

6 videos (Total 130 minutos)

Semana

Semana 6

12 horas para completar

Mixed Integer Programming

Mixed Integer Programming generalizes linear programming by allowing integer variables, which dramatically changes the complexity of the problems but also broadens the potential applications significantly. These lectures review how to model problems in mixed-integer programming and how to solve mixed-integer programs using branch and bound. Advanced techniques such as cutting planes and polyhedral cuts are also covered.

12 horas para completar

6 videos (Total 136 minutos)

Semana

Semana 7

11 horas para completar

Advanced Topics: Part I

These lectures cover some more advanced concepts in optimization. They introduce constraint-programming techniques for scheduling and routing.

11 horas para completar

2 videos (Total 51 minutos)

Semana

Semana 8

1 hora para completar

Advanced Topics: Part II

These lectures continues to cover some more advanced concepts in optimization. They introduce large neighborhood search, which often combines constraint programming and local search, and column generation which decomposes an optimization model into a master and pricing problem, using more complex variables.

1 hora para completar

2 videos (Total 32 minutos), 1 lectura

Reseñas

4.8

138 reseña

5 stars
89,49 %
4 stars
7,88 %
3 stars
1,14 %
2 stars
0,16 %
1 star
1,31 %

Principales reseñas sobre OPTIMIZACIÓN DISCRETA

por SK29 de may. de 2019

Exceptional coverage of optimization fundamentals. Learning of practical applied methods. Real university level course, no water down "data science". Absolutely love it! Thank you professor Pascal.

por JB21 de jul. de 2017

Great course, the teacher is very clear and often goes into sufficient depth for the student to understand concepts. Assignments are challenging and help understanding course content.

por M2 de jun. de 2020

This is, without a doubt, one of the most interesting courses I have ever taken. You will be challenged to create your own ideas and you will get to know what NP hard means in practice.

por GD1 de may. de 2019

I just completed the course. This an amazing course with an Outstanding professor and highly interesting, although difficult, assignments. Thanks for this! I am proud to have finished

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Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si compro el Certificado?
Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.
Is financial aid available?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el vínculo de Ayuda económica que está debajo del botón 'Inscribirse' a la izquierda. Se te pedirá que completes una solicitud y recibirás una notificación cuando se apruebe. Obtén más información.
• What are the pre-requisites for the class?
Good programming skills, knowledge of algorithms and linear algebra.
• What programming language will be used in this class?
A minimal knowledge of python is necessary to integrate with the course infrastructure. Outside of that, students are free to use any language of their choice.
• How difficult is this class?
A motivated student spending the time on the programming assignment will succeed in this class.
• Where can I get one of those T-Shirts?
At the discrete optimization store: http://www.zazzle.com.au/discreteoptimization

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Optimización discreta

Optimización discreta

Acerca de este Curso

Resultados profesionales del estudiante

29%

17%

Habilidades que obtendrás

Resultados profesionales del estudiante

29%

17%

Instructores

Professor Pascal Van Hentenryck

Dr. Carleton Coffrin

ofrecido por

Universidad de Melbourne

Programa - Qué aprenderás en este curso

Semana 1

Welcome

Semana 2

Knapsack

Semana 3

Constraint Programming

Semana 4

Local Search

Semana 5

Linear Programming

Semana 6

Mixed Integer Programming

Semana 7

Advanced Topics: Part I

Semana 8

Advanced Topics: Part II

Reseñas

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