By using ML surrogates to predict required system costs and performance indicators, we can approximate the nonlinearities in the GDP to generate an efficient mixed-integer linear programming (MILP) ...

In this paper, we develop a general regularization-based continuous optimization framework for the maximum clique problem. In particular, we consider a broad class of regularization terms that can be ...

We also show a family of linear programs that characterize all algorithms that are allowed to choose J candidates and gain profit from the K best candidates. We believe that a linear programming based ...

Disadvantages Linearity assumption: Linear programming assumes that relationships between variables are linear, which may not always be realistic in real-world problems.

Karmarkar (1984) found the first method of the interior point algorithm, so linear programming appeared as a dynamic field of research. Soon after, the interior point algorithm was able to resolve ...

We develop a linear programming formulation to address this global inference problem and evaluate it in the context of simultaneously learning named entities and relations.

A general form of fuzzy linear fractional programming problem with trapezoidal fuzzy numbers is proposed by Das [12]. Lotfi et al. [13] proposed a method to obtain the approximate solution of fully ...

Mixed-integer linear programming (MILP) is often used for system analysis and optimization as it presents a flexible and powerful method for solving large, complex problems such as the case with ...