Lecture

Optimization Programs: Piecewise Linear Cost Functions

Description

This lecture covers the formulation of optimization programs for minimizing piecewise linear cost functions subject to linear constraints, exploring convex and concave cases. The instructor presents examples and explains how to represent convex and concave functions in optimization programs.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructor

commodo occaecat

Labore cillum amet duis commodo consectetur ea ut est labore irure sint. Aliqua nulla ex do duis. Eiusmod duis elit incididunt deserunt quis nulla quis voluptate sunt tempor amet exercitation consequat ad. Culpa in id ad Lorem esse non occaecat mollit laboris occaecat aute pariatur do qui. Veniam consequat quis aliqua esse tempor magna. Sit nisi nisi dolore sint cillum enim et excepteur eiusmod occaecat.

Official source

https://mediaspace.epfl.ch/media/0_7fvu3712

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Ontological neighbourhood

Mathematics

Discrete mathematics: Graph theory

Mathematical logic: Set theory

Theoretical computer science

Algorithms and data structures: Combinatorial optimization

Computer engineering

Computer programming: Compilers

Related lectures (241)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.