import Pkg
Pkg.add("Convex")

# Let us first make the Convex.jl module available
using Convex, SCS
# Generate random problem data
m = 4;  n = 5
A = randn(m, n); b = randn(m, 1)
# Create a (column vector) variable of size n x 1.
x = Variable(n)
# The problem is to minimize ||Ax - b||^2 subject to 0 <= x <= 1
# This can be done by: minimize(objective, constraints)
problem = minimize(sumsquares(A * x - b), [x >= 0, x <= 1])
# Solve the problem by calling solve!
solve!(problem, SCS.Optimizer)
# Check the status of the problem
problem.status
# Get the optimal value
problem.optval

julia> using JuMP, Convex, Clarabel
julia> model = Model(() -> Convex.Optimizer(Clarabel.Optimizer));
julia> set_silent(model)
julia> @variable(model, x >= 1);
julia> @variable(model, t);
julia> @constraint(model, t >= exp(x))
t - exp(x) ≥ 0
julia> @objective(model, Min, t);
julia> optimize!(model)
julia> value(x), value(t)
(0.9999999919393833, 2.7182818073461403)