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

# Convex Optimization in Julia

## Convex.jl team

• Convex.jl: Madeleine Udell, Karanveer Mohan, David Zeng, Jenny Hong

## Collaborators/Inspiration:

• CVX: Michael Grant, Stephen Boyd
• CVXPY: Steven Diamond, Eric Chu, Stephen Boyd
• JuliaOpt: Miles Lubin, Iain Dunning, Joey Huchette
# initial package installation
# Make the Convex.jl module available
using Convex, SparseArrays, LinearAlgebra
using SCS # first order splitting conic solver [O'Donoghue et al., 2014]

# Generate random problem data
m = 50;  n = 100
A = randn(m, n)
x♮ = sprand(n, 1, .5) # true (sparse nonnegative) parameter vector
noise = .1*randn(m)    # gaussian noise
b = A*x♮ + noise      # noisy linear observations

# Create a (column vector) variable of size n.
x = Variable(n)

# nonnegative elastic net with regularization
λ = 1
μ = 1
problem = minimize(square(norm(A * x - b)) + λ*square(norm(x)) + μ*norm(x, 1),
x >= 0)

# Solve the problem by calling solve!
solve!(problem, SCSSolver(verbose=0))

println("problem status is ", problem.status) # :Optimal, :Infeasible, :Unbounded etc.
println("optimal value is ", problem.optval)
problem status is Optimal
optimal value is 32.8167651162629
using Interact, Plots
# Interact.WebIO.install_jupyter_nbextension() # might be helpful if you see WebIO warnings in Jupyter
@manipulate throttle=.1 for λ=0:.1:5, μ=0:.1:5
global A
problem = minimize(square(norm(A * x - b)) + λ*square(norm(x)) + μ*norm(x, 1),
x >= 0)
solve!(problem, SCSSolver(verbose=0))
histogram(evaluate(x), xlims=(0,3.5), label="x")
end

# Quick convex prototyping

## Variables

# Scalar variable
x = Variable()
Variable
size: (1, 1)
sign: real
vexity: affine
id: 164…885
# (Column) vector variable
y = Variable(4)
Variable
size: (4, 1)
sign: real
vexity: affine
id: 139…455
# Matrix variable
Z = Variable(4, 4)
Variable
size: (4, 4)
sign: real
vexity: affine
id: 131…058

# Expressions

Convex.jl allows you to use a wide variety of functions on variables and on expressions to form new expressions.

x + 2x
+ (affine; real)
├─ real variable (id: 164…885)
└─ * (affine; real)
├─ 2
└─ real variable (id: 164…885)
e = y[1] + logdet(Z) + sqrt(x) + minimum(y)
+ (concave; real)
├─ index (affine; real)
│  └─ 4-element real variable (id: 139…455)
├─ logdet (concave; real)
│  └─ 4×4 real variable (id: 131…058)
├─ geomean (concave; positive)
│  ├─ real variable (id: 164…885)
│  └─ [1.0]
└─ minimum (concave; real)
└─ 4-element real variable (id: 139…455)

### Examine the expression tree

e.children[2]
logdet (concave; real)
└─ 4×4 real variable (id: 131…058)

# Constraints

A constraint is convex if convex combinations of feasible points are also feasible. Equivalently, feasible sets are convex sets.

In other words, convex constraints are of the form

• convexExpr <= 0
• concaveExpr >= 0
• affineExpr == 0
x <= 0
<= constraint (affine)
├─ real variable (id: 164…885)
└─ 0
square(x) <= sum(y)
<= constraint (convex)
├─ qol_elem (convex; positive)
│  ├─ real variable (id: 164…885)
│  └─ [1.0]
└─ sum (affine; real)
└─ 4-element real variable (id: 139…455)
M = Z
for i = 1:length(y)
global M += rand(size(Z)...)*y[i]
end
M ⪰ 0
sdp constraint (affine)
└─ + (affine; real)
├─ 4×4 real variable (id: 131…058)
├─ * (affine; real)
│  ├─ 4×4 Array{Float64,2}
│  └─ index (affine; real)
│     └─ …
├─ * (affine; real)
│  ├─ 4×4 Array{Float64,2}
│  └─ index (affine; real)
│     └─ …
├─ * (affine; real)
│  ├─ 4×4 Array{Float64,2}
│  └─ index (affine; real)
│     └─ …
└─ * (affine; real)
├─ 4×4 Array{Float64,2}
└─ index (affine; real)
└─ …

# Problems

x = Variable()
y = Variable(4)
objective = 2*x + 1 - sqrt(sum(y))
constraint = x >= maximum(y)
p = minimize(objective, constraint)
minimize
└─ + (convex; real)
├─ * (affine; real)
│  ├─ 2
│  └─ real variable (id: 113…029)
├─ 1
└─ - (convex; negative)
└─ geomean (concave; positive)
├─ …
└─ …
subject to
└─ >= constraint (convex)
├─ real variable (id: 113…029)
└─ maximum (convex; real)
└─ 4-element real variable (id: 175…818)

current status: not yet solved
# solve the problem
solve!(p, SCSSolver(verbose=0))
p.status
:Optimal
x.value
0.2500000000385289
# can evaluate expressions directly
evaluate(objective)
1×1 Array{Float64,2}:
0.5000000000686586

## Pass to solver

call a MathProgBase solver suited for your problem class

to solve problem using a different solver, just import the solver package and pass the solver to the solve! method: eg

using Mosek
solve!(p, MosekSolver())

## Warmstart

# Generate random problem data
m = 50;  n = 100
A = randn(m, n)
x♮ = sprand(n, 1, .5) # true (sparse nonnegative) parameter vector
noise = .1*randn(m)    # gaussian noise
b = A*x♮ + noise      # noisy linear observations

# Create a (column vector) variable of size n.
x = Variable(n)

# nonnegative elastic net with regularization
λ = 1
μ = 1
problem = minimize(square(norm(A * x - b)) + λ*square(norm(x)) + μ*norm(x, 1),
x >= 0)
@time solve!(problem, SCSSolver(verbose=0))
λ = 1.5
@time solve!(problem, SCSSolver(verbose=0), warmstart = true)
  0.220074 seconds (4.11 k allocations: 3.317 MiB)
0.867662 seconds (996.42 k allocations: 54.282 MiB, 4.19% gc time)

# DCP examples

# affine
x = Variable(4)
y = Variable(2)
sum(x) + y[2]
+ (affine; real)
├─ sum (affine; real)
│  └─ 4-element real variable (id: 161…223)
└─ index (affine; real)
└─ 2-element real variable (id: 523…821)
2*maximum(x) + 4*sum(y) - sqrt(y[1] + x[1]) - 7 * minimum(x[2:4])
+ (convex; real)
├─ * (convex; real)
│  ├─ 2
│  └─ maximum (convex; real)
│     └─ 4-element real variable (id: 161…223)
├─ * (affine; real)
│  ├─ 4
│  └─ sum (affine; real)
│     └─ 2-element real variable (id: 523…821)
├─ - (convex; negative)
│  └─ geomean (concave; positive)
│     ├─ + (affine; real)
│     │  ├─ …
│     │  └─ …
│     └─ [1.0]
└─ - (convex; real)
└─ * (concave; real)
├─ 7
└─ minimum (concave; real)
└─ …
# not dcp compliant
log(x) + square(x)
+ (NotDcp; real)
├─ log (concave; real)
│  └─ 4-element real variable (id: 161…223)
└─ qol_elem (convex; positive)
├─ 4-element real variable (id: 161…223)
└─ 4×1 Array{Float64,2}
# $f$ is convex increasing and $g$ is convex
square(pos(x))
qol_elem (convex; positive)
├─ max (convex; positive)
│  ├─ 4-element real variable (id: 161…223)
│  └─ 0
└─ 4×1 Array{Float64,2}
# $f$ is convex decreasing and $g$ is concave
invpos(sqrt(x))
qol_elem (convex; positive)
├─ 4×1 Array{Float64,2}
└─ geomean (concave; positive)
├─ 4-element real variable (id: 161…223)
└─ 4×1 Array{Float64,2}
# $f$ is concave increasing and $g$ is concave
sqrt(sqrt(x))
geomean (concave; positive)
├─ geomean (concave; positive)
│  ├─ 4-element real variable (id: 161…223)
│  └─ 4×1 Array{Float64,2}
└─ 4×1 Array{Float64,2}