clingo[LP] extends the ASP solver clingo with linear constraints as dealt with in Linear Programming (LP).

Install

Install via conda:

  • conda install -c potassco -c conda-forge clingo-lp

CPLEX

The clingolp propagator requires an LP solver. The default is lpsolve but cplex can be used optionally.

IBM also provides a promotional version of cplex that is sufficient to solve small problems limited to 1000 variables and 1000 constraints.

  • conda install -c ibmdecisionoptimization cplex

To solve larger problems, you need to use the full version of CPLEX Studio.

Syntax

LP constraints can be expressed as follows:

LP constraints ClingoLP Syntax
w1x1+…+wnxn >= k &sum{w1*x1;…;wn*xn} >= k
domain(x)={l,…,u} &dom{l..u} = x
maximize: w1x1+…+wnxn &maximize{w1*x1;…;wn*x1} (minimize analogous)

To avoid syntax clashes, you must quote " real numbers. Instead of 1.5 write "1.5".

General usage of clingo[LP]

Basic call: clingoLP <encoding> <instance> <options>

Example: clingoLP example_encoding.lp example_instance.lp --show-lp-solution 0

  • Options
    • –show-lp-solution
      • Show LP solution and value of objective function
    • –lp-solver=cplx
      • Selects a LP solver (default lp-solver=lps)
    • –accuracy=n
      • Prints n decimal positions (default accuracy=1)
    • –epsilon=n,m
      • Set epsilon to convert lhs > k into lhs >= k+n*10-m (default epsilon=1,3)
    • –strict
      • Enables strict semantics
    • –trace
      • Enables detailed output of theory propagation
    • –core-confl=n
      • Searches for core conflicts if at least n% of the theory atoms are decided (default core-confl=20)
    • –prop-heur=n
      • Starts a solve call of the LP solver if at least n% of the theory atoms are decided (default prop-heur=0)
    • –ilp
      • Sets the LP solver to solve an Integer Linear Programming (ILP) problem
    • All clingo options
    • Number of solutions controlled via clingo

Publication