Boscia.jl

A solver for Mixed-Integer Convex Optimization that uses Frank-Wolfe methods for convex relaxations and a branch-and-bound algorithm.

Overview

The Boscia.jl solver combines (a variant of) the Frank-Wolfe algorithm with a branch-and-bound-like algorithm to solve mixed-integer convex optimization problems of the form $\min_{x ∈ C, x_I ∈ \mathbb{Z}^n} f(x)$, where $f$ is a differentiable convex function, $C$ is a convex and compact set, and $I$ is a set of indices of integral variables.

This approach is especially effective when we have a method to optimize a linear function over $C$ and the integrality constraints in a computationally efficient way. The set C can modelled using the Julia package MathOptInterface (or JuMP). We also implemented simple polytopes like the hypercube, the unit simplex and the probability simplex. Also, we intend to extend this list by combinatorial polytopes, e.g. the matching polytope.

The paper presenting the package with mathematical explanations and numerous examples can be found here:

Convex mixed-integer optimization with Frank-Wolfe methods: 2208.11010

Boscia.jl uses FrankWolfe.jl for solving the convex subproblems, Bonobo.jl for managing the search tree, and oracles optimizing linear functions over the feasible set, for instance calling SCIP or any MOI-compatible solver to solve MIP subproblems.

Installation

Add the Boscia stable release with:

import Pkg
Pkg.add("Boscia")

Or get the latest master branch with:

import Pkg
Pkg.add(url="https://github.com/ZIB-IOL/Boscia.jl", rev="main")

For the installation of SCIP.jl, see here. Note, for Windows users, you do not need to download the SCIP binaries, you can also use the installer provided by SCIP.

Getting started

Here is a simple example to get started. For more examples, see the examples folder in the package.

using Boscia
using FrankWolfe
using Random
using SCIP
using LinearAlgebra
import MathOptInterface
const MOI = MathOptInterface

n = 6

const diffw = 0.5 * ones(n)
o = SCIP.Optimizer()

MOI.set(o, MOI.Silent(), true)

x = MOI.add_variables(o, n)

for xi in x
    MOI.add_constraint(o, xi, MOI.GreaterThan(0.0))
    MOI.add_constraint(o, xi, MOI.LessThan(1.0))
    MOI.add_constraint(o, xi, MOI.ZeroOne())
end

lmo = FrankWolfe.MathOptLMO(o)

function f(x)
    return sum(0.5*(x.-diffw).^2)
end

function grad!(storage, x)
    @. storage = x-diffw
end

x, _, result = Boscia.solve(f, grad!, lmo, settings_bnb=Boscia.settings_bnb(verbose = true))

Boscia Algorithm.

Parameter settings.
	 Tree traversal strategy: Move best bound
	 Branching strategy: Most infeasible
	 FrankWolfe variant: Blended Pairwise Conditional Gradient
	 Line Search Method: Secant
	 Lazification: true
	 Lazification Tolerance: 2
	 Absolute dual gap tolerance: 1.000000e-06
	 Relative dual gap tolerance: 1.000000e-02
	 Frank-Wolfe subproblem tolerance: 1.000000e-02
	 Frank-Wolfe dual gap decay factor: 8.000000e-01
	 Additional kwargs: 
	 Total number of variables: 6
	 Number of integer variables: 6


-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
   Iter  Open          Bound      Incumbent      Gap (abs)      Gap (rel)       Time (s)      Nodes/sec      FW (ms)   LMO (ms)  LMO (calls c)   FW (its) #activeset  #shadow
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
*     1     2   0.000000e+00   7.500000e-01   7.500000e-01            Inf   2.000000e-03   1.500000e+03            1          1              4          2        1        0
    100    27   6.250000e-01   7.500000e-01   1.250000e-01   2.000000e-01   6.400000e-02   1.984375e+03            0          0            326          0        1        0
    127     0   7.500000e-01   7.500000e-01   0.000000e+00   0.000000e+00   7.300000e-02   1.739726e+03            0          0            380          0        1        0
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------


Solution Statistics.
	 Solution Status: Optimal (tree empty)
	 Primal Objective: 0.75
	 Dual Bound: 0.75
	 Dual Gap (relative): 0.0

Search Statistics.
	 Total number of nodes processed: 127
	 Total number of lmo calls: 380
	 Total time (s): 0.074
	 LMO calls / sec: 5135.135135135135
	 Nodes / sec: 1716.2162162162163
	 LMO calls / node: 2.9921259842519685

	 Total number of global tightenings: 0
	 Global tightenings / node: 0.0
	 Total number of local tightenings: 0
	 Local tightenings / node: 0.0
	 Total number of potential local tightenings: 0