IntervalConstraintProgramming.jl

This Julia package allows us to specify a set of constraints on real-valued variables, given by inequalities, and rigorously calculate (inner and outer approximations to) the feasible set, i.e. the set that satisfies the constraints.

The package is based on interval arithmetic using the IntervalArithmetic.jl package (co-written by the author), in particular multi-dimensional IntervalBoxes (i.e. Cartesian products of one-dimensional intervals).

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Basic usage

using IntervalArithmetic, IntervalArithmetic.Symbols
using IntervalConstraintProgramming
using IntervalBoxes
using Symbolics

vars = @variables x, y

C1 = constraint(x^2 + 2y^2 ≥ 1, vars)
C2 = constraint(x^2 + y^2 + x * y ≤ 3, vars)
C = C1 ⊓ C2

X = IntervalBox(-5..5, 2)

tolerance = 0.05
inner, boundary = pave(X, C, tolerance)

# plot the result:
using Plots

plot(collect.(inner), aspectratio=1, lw=0, label="inner");
plot!(collect.(boundary), aspectratio=1, lw=0, label="boundary")

• The inner, blue, region is guaranteed to lie inside the constraint set.
• The outer, white, region is guaranteed to lie outside the constraint set.
• The in-between, red, region is not known at this tolerance.

Author

• David P. Sanders, Departamento de Física, Facultad de Ciencias, Universidad Nacional Autónoma de México (UNAM)

References:

• Applied Interval Analysis, Luc Jaulin, Michel Kieffer, Olivier Didrit, Eric Walter (2001)
• Introduction to the Algebra of Separators with Application to Path Planning, Luc Jaulin and Benoît Desrochers, Engineering Applications of Artificial Intelligence 33, 141–147 (2014)

Acknowledements

Financial support is acknowledged from DGAPA-UNAM PAPIME grants PE-105911 and PE-107114, and DGAPA-UNAM PAPIIT grant IN-117214, and from a CONACYT-Mexico sabbatical fellowship. The author thanks Alan Edelman and the Julia group for hospitality during his sabbatical visit. He also thanks Luc Jaulin and Jordan Ninin for the IAMOOC online course, which introduced him to this subject.