Generic ILP vs Specialized 0-1 ILP for Haplotype Inference
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- TL;DR
- This study explores haplotype inference in genetics, focusing on the computationally challenging pure parsimony (HIPP) approach.
- Problem
- Although HIPP relies on a simple optimization criterion, finding a solution is a computationally hard problem.
- Method
- The paper compares the performance of specialized pseudo-Boolean optimization (PBO) solvers against generic integer linear programming (ILP) solvers across various HIPP models.
- Results
- The evaluation demonstrates how PBO and ILP solvers perform when applied to different HIPP models.
- Contributions
- Not specified in the abstract.
- Limitations
- Not specified in the abstract.
- Takeaways
- Specialized PBO solvers are more suitable for tackling the HIPP problem than generic ILP solvers.
- Applications
- Not specified in the abstract.
- Topics
- Haplotype Inference; Pure Parsimony (HIPP); Pseudo-Boolean Optimization (PBO); Integer Linear Programming (ILP)
- For industry
- Not specified in the abstract.
- Why it matters
- Not specified in the abstract.
Abstract
Abstract. Haplotype inference is an important and computationally challenging problem in genetics. A well-known approach to haplotype inference is pure parsimony (HIPP). Despite being based on a simple optimization criterion, HIPP is a computationally hard problem. Recent work has shown that approaches based on Boolean satisfiability namely pseudo-Boolean optimization (PBO), are very effective at tackling the HIPP problem. Extensive work on PBO-based HIPP approaches has been recently developed. Considering that the PBO problem, also known as 0-1 ILP problem, is a particular case of the integer linear programming (ILP) problem, generic ILP solvers can be considered. This paper compares the performance of PBO and ILP solvers on a variety of HIPP models. We conclude that specialized PBO solvers are more suitable than generic ILP solvers. 1