We present an object-oriented optimization framework that can be employed to solve small- and large-scale problems based on the concept of vectors and operators. By using such a strategy, we implement different iterative optimization algorithms that can be used in combination with architecture-independent vectors and operators, allowing the minimization of single-machine or cluster-based problems with a unique codebase. We implement a Python library following the described structure with a user-friendly interface that is designed to seamlessly scale to high-performance-computing (HPC) environments. We demonstrate its flexibility and scalability on multiple inverse problems, where convex and non-convex objective functions are optimized with ...
We generalize the standard method of solving inverse optimization problems to allow for the solution...
Non-euclidean versions of some primal-dual iterative optimization algorithms are presented. In these...
International audienceMany algorithms have been proposed during the last decade in order to deal wit...
We present an object-oriented optimization framework that can be employed to solve small- and large-...
Optimization plays an important role in solving many inverse problems. Indeed, the task of inversion...
This paper presents the main capabilities of IOSO (Indirect Optimization based on Self-Organization)...
Abstract We present pyOpt, an object-oriented frame-work for formulating and solving nonlinear const...
Inverse multi-objective combinatorial optimization consists of finding a minimal adjustment of the o...
Large optimization problems that involve either a large number of decision variables or many objecti...
Linear operators and optimization are at the core of many algorithms used in signal and image proces...
Abstract. Solution methods for very large scale optimization problems are addressed in this paper. I...
Une approche efficace pour la résolution de problèmes inverses consiste à définir le signal (ou l'im...
An object-oriented framework for Sequential Approximate Optimization (SAO) isproposed. The framework...
It is shown that finding a solution to a linear vector optimization problem which is efficient with ...
This paper studies the computational properties of the optimal subgradient algorithm (OSGA) for appl...
We generalize the standard method of solving inverse optimization problems to allow for the solution...
Non-euclidean versions of some primal-dual iterative optimization algorithms are presented. In these...
International audienceMany algorithms have been proposed during the last decade in order to deal wit...
We present an object-oriented optimization framework that can be employed to solve small- and large-...
Optimization plays an important role in solving many inverse problems. Indeed, the task of inversion...
This paper presents the main capabilities of IOSO (Indirect Optimization based on Self-Organization)...
Abstract We present pyOpt, an object-oriented frame-work for formulating and solving nonlinear const...
Inverse multi-objective combinatorial optimization consists of finding a minimal adjustment of the o...
Large optimization problems that involve either a large number of decision variables or many objecti...
Linear operators and optimization are at the core of many algorithms used in signal and image proces...
Abstract. Solution methods for very large scale optimization problems are addressed in this paper. I...
Une approche efficace pour la résolution de problèmes inverses consiste à définir le signal (ou l'im...
An object-oriented framework for Sequential Approximate Optimization (SAO) isproposed. The framework...
It is shown that finding a solution to a linear vector optimization problem which is efficient with ...
This paper studies the computational properties of the optimal subgradient algorithm (OSGA) for appl...
We generalize the standard method of solving inverse optimization problems to allow for the solution...
Non-euclidean versions of some primal-dual iterative optimization algorithms are presented. In these...
International audienceMany algorithms have been proposed during the last decade in order to deal wit...