This intrinsically non-convex problem is reformulated to a mixed-integer linear program, which is solved with a parallel implementation of branch-and-bound.
In this paper, we revisit the design and implementation of Branch-and-Bound (B&B) algorithms for solving large combinatorial optimization problems on GPU-enhanced multi-core machines.
In this paper, we revisit the design and implementation of Branch-and-Bound (B&B) algorithms for multi-core processors and Intel Xeon Phi coprocessors considering the offload mode as well as the native one.
(a) 2×2 MIMO-PD implementation, (b) one branch of the MIMO-PD composed by 2(M1,1 + M1,2) memoryless polynomials, and (c) memoryless polynomial implementation of two branches (conjugate and non-conjugate).
In the dynAlgorithmion of the optimization problem we use the Dynamic Programming [35], that is based on the idea of breaking down the problem into stages at which the decisions take place and finding a recurrence relation that takes us backward from one stage to the previous stage.
In this work, we revisit the design and implementation of the Branch-and-Bound (B&B) algorithm for heterogeneous environments combining multi-core processors with GPU accelerators.
For solving the linear programming relaxations and for a generic implementation of the branch-and-cut approach, we used the commercial packages IBM CPLEX (version 11.2) [2] and ILOG Concert Technology (version 2.7).
The species tree was generated with the PhyML 3.0 software [ 78] with aLRT implementation, for the calculation of branch supports as Chi2 based support.
The gene tree was generated with the PhyML 3.0 software [ 78] with aLRT implementation, for the calculation of branch supports as Chi2 based support.
These are computed by a tailored implementation of constraint orbital branching, a method designed to exploit symmetry in integer programs.
