Two different types of optimizers are available for linear problems: The default is an interior-point method, and the alternative is the simplex method (primal or dual). The optimizer can be selected using the parameter MSK_IPAR_OPTIMIZER. The Interior-point or the Simplex Optimizer? Given a ...
Data reduction can be used with any surrogate problem so long as the \varepsilon -level set of the surrogate problem contains all optimizers to the original problem. That is, we can use any feasible set \tilde{\mathcal {F}} and any objective function \tilde{Z}(.) as long as we can...
Neural networks and (mixed integer) linear programming have been brought together in the context of network fooling and model checking as proposed by Heo et al. and Modas et al. [31,51]. Already trained networks can be analyzed using LPs, see Anderson et al. [3] and there exist works w...
This method has been successfully applied to a number of molecules, has been shown to work more reliably than the previous optimizers, and can be extended to TS search. However, for the optimization of the linear response wavefunction, considerable additional effort is required. In this paper, ...
Mixed-Integer Linear Programming (MILP) has been explicitly applied in constructing automatic search algorithm in differential and linear cryptanalysis. The problem of MILP is a class of optimization problems derived from Linear Programming which aims to optimize an objective function under certain con- ...