All of the examples can be found in Jupyter notebook form here.

# Chebyshev center

Boyd & Vandenberghe, "Convex Optimization" Joëlle Skaf - 08/16/05

Adapted for Convex.jl by Karanveer Mohan and David Zeng - 26/05/14

The goal is to find the largest Euclidean ball (i.e. its center and radius) that lies in a polyhedron described by affine inequalites in this fashion: $P = \{x : a_i'*x \leq b_i, i=1,\ldots,m \}$ where $x \in \mathbb{R}^2$.

using Convex
using LinearAlgebra
import SCS

Generate the input data

a1 = [2; 1];
a2 = [2; -1];
a3 = [-1; 2];
a4 = [-1; -2];
b = ones(4, 1);

Create and solve the model

r = Variable(1)
x_c = Variable(2)
p = maximize(r)
p.constraints += a1' * x_c + r * norm(a1, 2) <= b;
p.constraints += a2' * x_c + r * norm(a2, 2) <= b;
p.constraints += a3' * x_c + r * norm(a3, 2) <= b;
p.constraints += a4' * x_c + r * norm(a4, 2) <= b;
solve!(p, SCS.Optimizer; silent_solver = true)
p.optval
0.4472102320727458

Generate the figure

x = range(-1.5, stop = 1.5, length = 100);
theta = 0:pi/100:2*pi;
using Plots
plot(x, x -> -x * a1 / a1 + b / a1)
plot!(x, x -> -x * a2 / a2 + b / a2)
plot!(x, x -> -x * a3 / a3 + b / a3)
plot!(x, x -> -x * a4 / a4 + b / a4)
plot!(
evaluate(x_c) .+ evaluate(r) * cos.(theta),
evaluate(x_c) .+ evaluate(r) * sin.(theta),
linewidth = 2,
)
plot!(
title = "Largest Euclidean ball lying in a 2D polyhedron",
legend = nothing,
)