Author joaquimg
Popularity
6 Stars
Updated Last
2 Years Ago
Started In
March 2020

## Introduction

Non-convex quadratic programs are extremely hard to solve. This problem class can be solved by Global Solvers such as Couenne. Another possibility is to rely on binary expasion of products terms that appear in the problem, in this case the problem is approximated and can be solved by off-the-shelf MIP solvers such as Cbc, CPLEX, GLPK, Gurobi, Xpress.

## Example

If one wants to solve the optimization problem with the package:

```# Max 2x + y
# s.t. x * y <= 4 (c)
#      x, y >= 1```

One should model as a quadratic program and simply wrap the solver with a `QuadraticToBinary.Optimizer`, with one extra requirement: all variables appearing in quadratic terms must be bounded above an below.

Therefore the new model can be:

```# Max 2x + y
# s.t. x * y <= 4 (c)
#      x, y >= 1
#      x, y <= 10```

### JuMP with Cbc solver

```using JuMP
using MathOptInterface
using Cbc

const MOI = MathOptInterface
const MOIU = MOI.Utilities

const optimizer = MOI.Bridges.full_bridge_optimizer(
MOIU.CachingOptimizer(
MOIU.UniversalFallback(MOIU.Model{Float64}()),
Cbc.Optimizer()), Float64)

model = Model(
optimizer))

@variable(model, 1 <= x <= 10)
@variable(model, 1 <= y <= 10)

@constraint(model, c, x * y <= 4)

@objective(model, Max, 2x + y)

optimize!(model)

termination_status(model)

primal_status(model)

objective_value(model) # ≈ 9.0

value(x) # ≈ 4.0
value(y) # ≈ 1.0

value(c) # ≈ 4.0```

### MathOptInterface with Cbc solver

```using MathOptInterface
const MOI = MathOptInterface
const MOIU = MOI.Utilities
using Cbc

const optimizer = MOI.Bridges.full_bridge_optimizer(
MOIU.CachingOptimizer(
MOIU.UniversalFallback(MOIU.Model{Float64}()),
Cbc.Optimizer()), Float64)

[MOI.ScalarAffineTerm(0.0, x)], [MOI.ScalarQuadraticTerm(1.0, x, y)], 0.0)

MOI.set(model, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),
MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([2.0, 1.0], [x, y]), 0.0))
MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)

MOI.optimize!(model)

MOI.get(model, MOI.TerminationStatus()) # config.optimal_status

MOI.get(model, MOI.PrimalStatus()) # MOI.FEASIBLE_POINT

MOI.get(model, MOI.ObjectiveValue()) # ≈ 9.0

MOI.get(model, MOI.VariablePrimal(), x) # ≈ 4.0
MOI.get(model, MOI.VariablePrimal(), y) # ≈ 1.0

MOI.get(model, MOI.ConstraintPrimal(), c) # ≈ 4.0```

Note that duals are not available because the problem was approximated as a MIP.

It is possible to change the precision of the approximations to the number `val`, for all variables:

`MOI.set(model, QuadraticToBinary.GlobalVariablePrecision(), val)`

Or, for each variable `vi`:

`MOI.set(model, QuadraticToBinary.VariablePrecision(), vi, val)`

The precision for each varible will be `val * (UB - LB)`. Where `UB` and `LB` are, respectively, the upper and lower bound of the variable.

For the sake of simplicity, the following two attributes are made available: `QuadraticToBinary.FallbackUpperBound` and `QuadraticToBinary.FallbackLowerBound`. As usual, these can be get and set with the `MOI.get` and `MOI.set` methods. These allow setting bounds used in variables that have no explicit upper bounds and need to be expanded.

## Reference

For more details on the formulation applied here see this paper:

``````@article{andrade2019enhancing,
title={Enhancing the normalized multiparametric disaggregation technique for mixed-integer quadratic programming},
author={Andrade, Tiago and Oliveira, Fabricio and Hamacher, Silvio and Eberhard, Andrew},
journal={Journal of Global Optimization},
volume={73},
number={4},
pages={701--722},
year={2019},
publisher={Springer}
}
``````

### Used By Packages

No packages found.