Massively-parallel primal heuristics for (M)IP solvers

Massively-parallel primal heuristics for (M)IP solvers

Globally optimal mixed integer programming (MIP) solvers such as Gurobi or CPLEX rely on primal heuristics to

i. create decent starting solutions

ii. create descent upper bounds on the objective for early bounding.

Some rely on LP solvers, but some especially popular heuristics are purely combinatorial such as genetic algorithms, local search or taboo search.

In this thesis, your goal is to implement a given set of heuristics for general-purpose (M)IPs on GPUs and compare yourself to those packages w.r.t solution quality and processing time. Ideally, this results in a software package, that can be added to academic solvers.