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.

# 이산형 최적화

제공자:

## 이산형 최적화

## About this Course

### 학습자 경력 결과

## 40%

## 40%

## 17%

### 귀하가 습득할 기술

### 학습자 경력 결과

## 40%

## 40%

## 17%

#### 공유 가능한 수료증

#### 100% 온라인

#### 유동적 마감일

#### 중급 단계

#### 완료하는 데 약 33시간 필요

#### 영어

## 강의 계획 - 이 강좌에서 배울 내용

**완료하는 데 2시간 필요**

## 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.

**완료하는 데 2시간 필요**

**4개의 동영상**

**3개의 읽기 자료**

**완료하는 데 7시간 필요**

## 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.

**완료하는 데 7시간 필요**

**9개의 동영상**

**완료하는 데 17시간 필요**

## 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.

**완료하는 데 17시간 필요**

**13개의 동영상**

**1개의 읽기 자료**

**완료하는 데 13시간 필요**

## 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.

**완료하는 데 13시간 필요**

**10개의 동영상**

### 검토

#### 4.9

##### 이산형 최적화의 최상위 리뷰

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.

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

I like the instructor teaching approach and the evaluation system, the subject itself took me a lot of effort and i think the LNS technique should be teached just after local search.

Excellent course! The course video are very clear and build on each other as the course progress. It is well structured. The assignments have the right amount of difficulty.

Very good course for operation research aspirants. Assignments are very good to understand the problems and the complexity of the problems.Thanks to the team

i wish there was 6 start rating so i can give this prof his due, he made a very complicated subject look very simple and easy to understand thanks a million

Awesome course, learned a lot for solving NP hard problem. It could be hard for people without basic algorithm and data structure background.

The course Materials and instructors are awesome.\n\nThis course is very good and essential in my point of view for researchers.

Too good course! One of the very best courses on Coursera!\n\nThank you so much. It is a wonder to follow your course.

I learned a lot in this course and it's definitely well managed and well taught by an amazing professor.

Very thorough and nice course. Prof. Pascal can make even very difficult things funny and interesting!!

Best course I've taken on Coursera so far. Really challenges you to learn and apply algorithms/solvers.

### 멜버른 대학교 정보

## 자주 묻는 질문

강의 및 과제를 언제 이용할 수 있게 되나요?

강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

이 수료증을 구매하면 무엇을 이용할 수 있나요?

수료증을 구매하면 성적 평가 과제를 포함한 모든 강좌 자료에 접근할 수 있습니다. 강좌를 완료하면 전자 수료증이 성취도 페이지에 추가되며, 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 콘텐츠만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

환불 규정은 어떻게 되나요?

재정 지원을 받을 수 있나요?

• 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

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.