Constraints can be expressed as linear equations or inequalities involving decision variables and constants. For instance, if you want to maximize the profit from selling two products, X and Y ...

Cutting plane method is a technique for solving linear programming problems that involve integer variables. It works by iteratively adding linear inequalities, called cuts, to the original problem ...

As in all linear programming models, you first create linear inequalities out of the information you have about any constraints. In the case of profit maximisation or loss minimisation, for example, ...

How to solve linear programming and quadratic programming with inequality constraint only? For LP, I tried to use OSQP and pass the objective as (None, -c), the equality constraint as (None, None), ...

PROC NLP . The NLP procedure (NonLinear Programming) offers a set of optimization techniques for minimizing or maximizing a continuous nonlinear function f(x) of n decision variables, x = (x 1, ... ,x ...

Active Set Methods The parameter vector can be subject to a set of m linear equality and inequality constraints: The coefficients a ij and right-hand sides b i of the equality and inequality ...

Interest in dense sensor networks due to falling price and reduced size has motivated research in sensor location in recent years. While many algorithms can be found in the literature, no benchmark ...

Description: lineqGPR is a package for Gaussian process interpolation, regression and simulation under linear inequality constraints based on (López-Lopera et al., 2017). The constrained models are ...

Abstract. In quantitative decision analysis, an analyst applies mathematical models to make decisions. Frequently these models involve an optimization problem to determine the values of the decision ...

Interest in dense sensor networks due to falling price and reduced size has motivated research in sensor location in recent years. To our knowledge, the algorithm which achieves the best performance ...