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Update of examples (wip) #157

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merged 4 commits into from
Nov 4, 2023
Merged

Update of examples (wip) #157

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855e005
added cubeBLMO
pokutta Nov 3, 2023
b3d5d7b
update nonlinear.jl to use CubeBLMO
pokutta Nov 3, 2023
f6b7e05
Merge branch 'main' into update_of_examples
matbesancon Nov 3, 2023
92825dc
few more updates to examples
pokutta Nov 4, 2023
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2 changes: 2 additions & 0 deletions examples/low_dim_in_high_dim.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,8 @@ using Distributions
import MathOptInterface
const MOI = MathOptInterface

include("cube_blmo.jl")

# The example from "Optimizing a low-dimensional convex function over a high-dimensional cube"
# by Christoph Hunkenschröder, Sebastian Pokutta, Robert Weismantel
# https://arxiv.org/abs/2204.05266.
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43 changes: 30 additions & 13 deletions examples/nonlinear.jl
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@@ -1,4 +1,3 @@
using SCIP
using FrankWolfe
using LinearAlgebra
import MathOptInterface
Expand All @@ -8,23 +7,41 @@ import Bonobo
using Printf
using Dates

const MOI = MathOptInterface
# using SCIP
# const MOI = MathOptInterface

n = 5
include("cube_blmo.jl")

n = 40
seed = 10

Random.seed!(seed)

o = SCIP.Optimizer()
MOI.set(o, MOI.Silent(), true)
MOI.empty!(o)
x = MOI.add_variables(o, n)
for xi in x
MOI.add_constraint(o, xi, MOI.ZeroOne())
MOI.add_constraint(o, xi, MOI.GreaterThan(0.0))
MOI.add_constraint(o, xi, MOI.LessThan(1.0))
################################################################
# alternative implementation of LMO using MOI and SCIP
################################################################
# o = SCIP.Optimizer()
# MOI.set(o, MOI.Silent(), true)
# MOI.empty!(o)
# x = MOI.add_variables(o, n)
# for xi in x
# MOI.add_constraint(o, xi, MOI.ZeroOne())
# MOI.add_constraint(o, xi, MOI.GreaterThan(0.0))
# MOI.add_constraint(o, xi, MOI.LessThan(1.0))
# end
# lmo = FrankWolfe.MathOptLMO(o)


################################################################
# LMO via CubeBLMO
################################################################
int_vars = collect(1:n)
bounds = Boscia.IntegerBounds()
for i in 1:n
push!(bounds, (i, 0.0), :greaterthan)
push!(bounds, (i, 1.0), :lessthan)
end
lmo = FrankWolfe.MathOptLMO(o)
lmo = CubeBLMO(n, int_vars, bounds)

const A = let
A = randn(n, n)
Expand All @@ -47,6 +64,6 @@ function grad!(storage, x)
return mul!(storage, A, y, -2, 2)
end

x, _, _ = Boscia.solve(f, grad!, lmo, verbose=true, print_iter=1)
x, _, _ = Boscia.solve(f, grad!, lmo, verbose=true, print_iter=500)

@show x
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