In recent years, emphasis has been placed on generating quality representations of the nondominated set of multiobjective programming problems. This manuscript presents two methods for generating discrete representations with equidistant points for multiobjective programs with solution sets determined by convex cones. The Bilevel Controlled Spacing (BCS) method has a bilevel structure with the lower-level generating the nondominated points and the upper-level controlling the spacing. The Constraint Controlled Spacing (CCS) method is based on the epsilon-constraint method with an additional constraint to control the spacing of generated points. Both methods (under certain assumptions) are proven to produce (weakly) nondominated points. ...
The well-known result stating that any non-convex quadratic problem over the non-negative orthant wi...
Convex programming has been a research topic for a long time, both theoretically and algorithmically...
The efficiency of modern optimization methods, coupled with increasing computational resources, has ...
Within the past ten years, more emphasis has been placed on generating discrete represen-tations of ...
During the last few decades, multiobjective programming has received much attention for both its num...
This research effort focuses on the acquisition of polyhedral outer-approximations to the convex hul...
Many real-world applications involve multiple competing objectives, but due to conflict between the ...
In this paper we address the problem of representing the continuous but non-convex set of nondominat...
Le but de cette thèse est de proposer des méthodes générales afin de contourner l’intractabilité de ...
We introduce a novel approximation method for multiobjective optimization problems called PAINT–SiC...
In this thesis, we examine optimization problems with a constraint that allows for only a certain nu...
Linear programming (LP) and semidefinite programming (SDP) are among the most important tools in Ope...
Multiobjective quadratic programs (MOQPs) are appealing since convex quadratic programs have elegant...
We propose a new approach to convex nonlinear multiobjective optimization that captures the geometry...
In Chapter 2 of the thesis, we study cut generating functions for conic sets. Our first main result ...
The well-known result stating that any non-convex quadratic problem over the non-negative orthant wi...
Convex programming has been a research topic for a long time, both theoretically and algorithmically...
The efficiency of modern optimization methods, coupled with increasing computational resources, has ...
Within the past ten years, more emphasis has been placed on generating discrete represen-tations of ...
During the last few decades, multiobjective programming has received much attention for both its num...
This research effort focuses on the acquisition of polyhedral outer-approximations to the convex hul...
Many real-world applications involve multiple competing objectives, but due to conflict between the ...
In this paper we address the problem of representing the continuous but non-convex set of nondominat...
Le but de cette thèse est de proposer des méthodes générales afin de contourner l’intractabilité de ...
We introduce a novel approximation method for multiobjective optimization problems called PAINT–SiC...
In this thesis, we examine optimization problems with a constraint that allows for only a certain nu...
Linear programming (LP) and semidefinite programming (SDP) are among the most important tools in Ope...
Multiobjective quadratic programs (MOQPs) are appealing since convex quadratic programs have elegant...
We propose a new approach to convex nonlinear multiobjective optimization that captures the geometry...
In Chapter 2 of the thesis, we study cut generating functions for conic sets. Our first main result ...
The well-known result stating that any non-convex quadratic problem over the non-negative orthant wi...
Convex programming has been a research topic for a long time, both theoretically and algorithmically...
The efficiency of modern optimization methods, coupled with increasing computational resources, has ...