## E4650 Convex Optimization for Engineering
Department of Electrical Engineering, Columbia University
This is the Summer A, 2021 course homepage for EEOR E4650: Convex Optimization for Electrical Engineering.
## Basic information**Format**: Hybrid**Location:**TBA**Lectures**: Tuesday & Thursday, 1:10-3:40 pm.**Recitations**: As and when needed**Instructor**: James Anderson (james.anderson@columbia.edu)**TA:**TBA**Office hours**: James: TBA
## Course announcements**29 March:**Welcome to E4650, Summer A’21
## Overview## Course descriptionYou will learn to recognize and solve convex optimization problems that arise in applications spanning engineering, mathematics, and computer science. The course can be divided into three parts: theory, applications, and algorithms; A tentative list of topics can be seen below in the schedule. Application areas we will consider include signal processing, statistics and machine learning, finance, energy systems, and optimal control. ## Intended audienceThis course should benefit anyone who uses or will use scientific computing, data science, or optimization in engineering or related work (e.g., machine learning, finance). Within SEAS, people from the following departments and fields: Electrical Engineering (especially areas like signal processing, smart energy, communications, control); Applied Physics & Applied Math (numerical analysis, high performance computing), Civil Engineering & Engineering Mechanics (structural analysis, optimization, design); Computer Science (especially machine learning, computer graphics, algorithms & complexity, computational geometry); Mechanical Engineering (robotics, control, fluid mechanics); Industrial Engineering & Operations Research. The course may be useful to students and researchers in several other fields as well: Mathematics, Statistics, Finance, Economics. ## TextbookThis course closely follows The following books are useful as additional reference texts. Laurent El Ghaoui's lecture notes *Convex Optimization Theory*, D. Bertsekas (Athena Scientific)*Lectures on Convex Optimization*, Y. Nesterov (Springer)
## ScheduleThe tentative syllabus is set out below. Note that the current timetable is only a rough guide, and that there will be an in-class midterm which is not shown. Chapter numbers refer to the textbook
## Course organization## PrerequisitesStudents should have taken APMA E3101 or be comfortable with basic linear algebra at the level of Chapter 2 of Laurent El Ghaoui's notes as well as basic probability. In addition, familiarity with basic programming will be necessary to complete some homework questions. One of CVX (Matlab), CVXPY (Python), CVXR (R), or Convex.jl (Julia) will be used to write simple scripts. No prior knowledge of CVX is assumed, a recitation on this topic will be provided. ## Course requirementsHomework assignments: Approximately 1 homework per week, due in 10 days after release. Literature review: A list of topics will be provided, students will be expected to read around the topic, recreate results, and write a short report. Recitations: As and when needed. Final exam: The format is a 48-hour take home exam scheduled for the last week of class.
## GradingHomework 40%, Literature review 20%, Final Exam 40%.
## HomeworkAll homework must be submitted via Courseworks. You are allowed to work on homework in small groups, but the work you submit (including code) must be your own. |