# Convex.jl - Convex Optimization in Julia

Convex.jl is a Julia package for Disciplined Convex Programming (DCP).

Convex.jl makes it easy to describe optimization problems in a natural, mathematical syntax, and to solve those problems using a variety of different (commercial and open-source) solvers.

Convex.jl can be used to solve:

- linear programs
- mixed-integer linear programs and mixed-integer second-order cone programs
- DCP-compliant convex programs including
- second-order cone programs (SOCP)
- exponential cone programs
- semidefinite programs (SDP)

## Resources for getting started

There are a few ways to get started with Convex:

- Read the Installation guide
- Read the introductory tutorial Quick Tutorial
- Read the list of Supported Operations
- Browse some of our examples

Need help? Join the community forum to search for answers to commonly asked questions.

Before asking a question, make sure to read the post make it easier to help you, which contains a number of tips on how to ask a good question.

## How the documentation is structured

Having a high-level overview of how this documentation is structured will help you know where to look for certain things.

**Examples**contain worked examples of solving problems with Convex. Start here if you are new to Convex, or you have a particular problem class you want to model.The

**Manual**contains short code-snippets that explain how to achieve specific tasks in Convex. Look here if you want to know how to achieve a particular task.The

**Developer docs**section contains information for people contributing to Convex development. Don't worry about this section if you are using Convex to formulate and solve problems as a user.