Abstract: An optimization problem is the problem of finding the best solution from all feasible solutions. Solving optimization problems can be performed by heuristic algorithms or classical ...

Abstract: This paper develops a neural network architecture and a new processing method for solving in real time, the nonlinear equality constrained multiobjective optimization problem (NECMOP), where ...

HiOp is an optimization solver for solving certain mathematical optimization problems expressed as nonlinear programming problems. HiOp is a lightweight HPC solver that leverages application's ...

PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's renowned derivative-free optimization methods, i ...

Break even and profitability analysis is a classical and widely used topic in business analysis. Break-Even Point or point of equilibrium is the point of sales volume making neither a profit nor a ...

To fully tap the abilities of renewables in reactive power optimization, this paper develops a detailed model for the power regulation capabilities of wind turbines and photovoltaic units and studies ...

We present three possible strategies to effectively incorporate geological and/or geophysical constraints into deep neural networks (DNNs). They help address the main challenges of poor ...

Center on Stochastic Modeling, Optimization, and Statistics (COSMOS), The University of Texas at Arlington, Arlington, TX, USA. Quantitative decision analysis involves notions of comparison and ...

This paper discusses techniques for solving discrete optimization problems using quantum annealing. Practical issues likely to affect the computation include precision limitations, finite temperature, ...