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ZIB-IOL / Boscia.jl

Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations
MIT License
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first-order-methods frank-wolfe julia mixed-integer-programming nonlinear-optimization optimization optimization-algorithms

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Boscia.jl

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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 ∈ 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.

They are especially useful when we have a method to optimize a linear function over C and the integrality constraints in a compuationally efficient way. C is specified using the MathOptInterface API or any DSL like JuMP implementing it.

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

Convex 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")

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, verbose = true)

Boscia Algorithm.

Parameter settings.
     Tree traversal strategy: Move best bound
     Branching strategy: Most infeasible
     Absolute dual gap tolerance: 1.000000e-06
     Relative dual gap tolerance: 1.000000e-02
     Frank-Wolfe subproblem tolerance: 1.000000e-05
     Total number of varibales: 6
     Number of integer variables: 0
     Number of binary variables: 6

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
   Iteration       Open          Bound      Incumbent      Gap (abs)      Gap (rel)       Time (s)      Nodes/sec        FW (ms)       LMO (ms)  LMO (calls c)   FW (Its)   #ActiveSet  Discarded
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
*          1          2  -1.202020e-06   7.500000e-01   7.500012e-01            Inf   3.870000e-01   7.751938e+00            237              2              9         13            1          0
         100         27   6.249998e-01   7.500000e-01   1.250002e-01   2.000004e-01   5.590000e-01   2.271914e+02              0              0            641          0            1          0
         127          0   7.500000e-01   7.500000e-01   0.000000e+00   0.000000e+00   5.770000e-01   2.201040e+02              0              0            695          0            1          0
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Postprocessing

Blended Pairwise Conditional Gradient Algorithm.
MEMORY_MODE: FrankWolfe.InplaceEmphasis() STEPSIZE: Adaptive EPSILON: 1.0e-7 MAXITERATION: 10000 TYPE: Float64
GRADIENTTYPE: Nothing LAZY: true lazy_tolerance: 2.0
[ Info: In memory_mode memory iterates are written back into x0!

----------------------------------------------------------------------------------------------------------------
  Type     Iteration         Primal           Dual       Dual Gap           Time         It/sec     #ActiveSet
----------------------------------------------------------------------------------------------------------------
  Last             0   7.500000e-01   7.500000e-01   0.000000e+00   1.086583e-03   0.000000e+00              1
----------------------------------------------------------------------------------------------------------------
    PP             0   7.500000e-01   7.500000e-01   0.000000e+00   1.927792e-03   0.000000e+00              1
----------------------------------------------------------------------------------------------------------------

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: 699
     Total time (s): 0.58
     LMO calls / sec: 1205.1724137931035
     Nodes / sec: 218.96551724137933
     LMO calls / node: 5.503937007874016