The purpose of this paper is to propose a Mizuno-Todd-Ye predictor-corrector (MTY-PC) infeasible-interior-point method (infeasible-IPM) for symmetric optimization (SO) by using Euclidean Jordan algebra (EJA).
Seiford and Zhu [15] presented conditions that would render the super-efficiency method infeasible.
The significant computational cost of evaluating posterior model probabilities renders traditional Bayesian methods infeasible when the model space becomes large.
In this paper, we propose a Mizuno-Todd-Ye predictor-corrector infeasible-interior-point method for symmetric optimization using the arc-search strategy.
The main obstacle for training the model parameters is the non-smoothness of the objective function of LFM, which makes traditional parameter optimization methods infeasible.
In addition, the search space for feature selection problems with large-size complex features is huge, which makes many heuristic search methods infeasible.
The benefits of the FBA approach are that detailed kinetic information such as concentrations are not required, allowing the simulation of large models that would be infeasible using alternative methods.
However, this method is infeasible for ad hoc and sensor networks considering its computational complexity.
On the other hand, the total enumeration as a solution method is infeasible for medium and large scale problems.
