Optimized solvers for the Boolean Satisfiability (SAT) prob-
lem have many applications in areas such as hardware and
software verification, FPGA routing, planning, etc. Further
uses are complicated by the need to express "counting con-
straints" in conjunctive normal form (CNF). Expressing such
constraints by pure CNF leads to more complex SAT
instances. Alternatively, those constraints can be handled by
Integer Linear Programming (ILP), but generic ILP solvers
may ignore the Boolean nature of 0-1 variables. Therefore
specialized 0-1 ILP solvers extend SAT solvers to handle
these so-called "pseudo-Boolean" constraints.
This work provides an update on the on-going competi-
tion between generic ILP techniques and specialized 0-1 ILP
techniques. To make a fair comparison, we generalize recent
ideas for fast SAT-solving to more general 0-1 ILP problems
that may include counting constraints and optimization.
Another aspect of our comparison is evaluation on 0-1 ILP
benchmarks that originate in Electronic Design Automation
(EDA), but that cannot be directly solved by a SAT solver.