Add to ORKGThis work focuses on the approximation of bivariate functions into piecewise linear ones with a minimal number of pieces and under a bounded approximation error. Applications include the approximation of mixed integer nonlinear optimization problems into mixed integer linear ones that are in general easier to solve. A framework to build dedicated linearization algorithms is introduced, and a comparison to the state of the art heuristics shows their efficiency
AbstractThis paper considers the problem of optimizing a continuous nonlinear objective function sub...
This paper is concerned with a multiresolution approach to the piecewise-linear approximation of mul...
We discuss two models from the literature that have been developed to formulate piecewise linear app...
This work focuses on the approximation of bivariate functions into piecewise linear ones with a mini...
We develop explicit, piecewise-linear formulations of functions f(x):ℝn{mapping}ℝ, n ≤ 3, that are d...
International audienceVarious optimization problems result from the introduction of nonlinear terms ...
International audienceVarious optimization problems result from the introduction of nonlinear terms ...
In recent years, the increased efficiency of mixed integer linear programming (MILP) software tools ...
In recent years, the increased efficiency of mixed integer linear programming (MILP) software tools ...
This work considers non-convex mixed integer nonlinear programming where nonlinearity comes from the...
We study the modeling of non-convex piecewise linear functions as Mixed Integer Programming (MIP) pr...
This work considers non-convex mixed integer nonlinear programming where nonlinearity comes from the...
In this paper, we address the problem of approximating and over/under-estimating univariate function...
In this paper, we address the problem of approximating and over/under-estimating univariate function...
AbstractWe study the problem of optimizing nonlinear objective functions over bipartite matchings. W...
AbstractThis paper considers the problem of optimizing a continuous nonlinear objective function sub...
This paper is concerned with a multiresolution approach to the piecewise-linear approximation of mul...
We discuss two models from the literature that have been developed to formulate piecewise linear app...
This work focuses on the approximation of bivariate functions into piecewise linear ones with a mini...
We develop explicit, piecewise-linear formulations of functions f(x):ℝn{mapping}ℝ, n ≤ 3, that are d...
International audienceVarious optimization problems result from the introduction of nonlinear terms ...
International audienceVarious optimization problems result from the introduction of nonlinear terms ...
In recent years, the increased efficiency of mixed integer linear programming (MILP) software tools ...
In recent years, the increased efficiency of mixed integer linear programming (MILP) software tools ...
This work considers non-convex mixed integer nonlinear programming where nonlinearity comes from the...
We study the modeling of non-convex piecewise linear functions as Mixed Integer Programming (MIP) pr...
This work considers non-convex mixed integer nonlinear programming where nonlinearity comes from the...
In this paper, we address the problem of approximating and over/under-estimating univariate function...
In this paper, we address the problem of approximating and over/under-estimating univariate function...
AbstractWe study the problem of optimizing nonlinear objective functions over bipartite matchings. W...
AbstractThis paper considers the problem of optimizing a continuous nonlinear objective function sub...
This paper is concerned with a multiresolution approach to the piecewise-linear approximation of mul...
We discuss two models from the literature that have been developed to formulate piecewise linear app...