This paper presents a methodology to find optimal solutions for linear programming problems on imprecise conditions. By using α-cuts, the cumulative membership function and the classic fuzzy linear ...

This paper shows an alternative methodology to find optimal solutions of a linear programming problem defined in a fuzzy environment. The classical fuzzy linear programming (FLP) problem is treated by ...

Learn how to estimate linear regression model parameters in machine learning using ordinary least squares and gradient descent. Compare their advantages and disadvantages.

Learn some tips and tricks to overcome common challenges when implementing linear programming, such as choosing the right solver, handling non-linearities, dealing with large-scale models, and ...

This repository demonstrates how to use Linear programming using python package called pulp for optimization problem ... specifically how the flow of a liquid through a cylindrical pipe is influenced ...

Unlike S-parameters, however, they are applicable to both large-signal and small-signal conditions, and can be used for linear and nonlinear components. They correctly characterize impedance ...

In this study, an interval-parameter fuzzy robust nonlinear programming (IFRNP) model was developed for water quality management to deal with such difficulties. The developed model ... B. W. Baetz and ...

‘ Well, Linear Programming (LP) is, in general, demanding and time-consuming. This is perhaps the reason why it has taken developers an eternity to create linear programming software. But things seem ...

Scenario: Solve a linear program with an objective function parameterized by a constant. Tasks: Use the Simplex Method to solve the initial problem for a fixed value of the parameter. Analyze how ...

Parameter mixing is often referred to as “crossover” in the ES-community and will be explained later in more detail. If the trial vector yields a lower cost function value than the ... Kessler, A. and ...