Add to ORKGAbstract Recent years witnessed the proliferation of the notion of sparsity and its applications in operations research models. To bring to the attention and raise the interest of the operations research community on this topic, we present in this tutorial a wide range of complex models that admit sparse yet effective solutions. Our examples range from compressed sensing and process flexibility to queuing applications, and from equation systems and optimization problems to game theory models. Keywords sparse; complex; equations; quadratic program; game theory; two-stage stochastic pro-gram; flexibility 1
In the last decade, the demand for statistical and computation methods for data analysis that involv...
The pioneering work on parameter orthogonalization by Cox and Reid (1987) is presented as an inducem...
We introduce a framework for sparsity structures defined via graphs. Our approach is flexible and ge...
It is a well known experience that for sparse structures one can find fast algorithm for some proble...
In many situations, a solution to a practical problem is sparse, i.e., corresponds to the case when ...
A vector or matrix is said to be sparse if the number of non-zero elements is significantly smaller ...
The aim of this work package (WP) is to explore approaches to learn structured sparse models, that i...
Sparse estimation methods are aimed at using or obtaining parsimo-nious representations of data or m...
This paper proposes a model with boundedly rational features in which the decision-maker (DM) builds...
International audienceSolving zero-sum matrix games is polynomial, because it boils down to linear p...
The aim of this thesis is to conduct a general investigation in the field of sparse matrices, to inv...
Arguably one of the most notable forms of the principle of parsimony was formulated by the philosoph...
This paper aims to provide a sharp excess risk guarantee for learning a sparse linear model without ...
2018-08-13This dissertation contains three individual collaborative studies for sparse learning prob...
We introduce a framework for sparsity structures defined via graphs. Our approach is flexible and ge...
In the last decade, the demand for statistical and computation methods for data analysis that involv...
The pioneering work on parameter orthogonalization by Cox and Reid (1987) is presented as an inducem...
We introduce a framework for sparsity structures defined via graphs. Our approach is flexible and ge...
It is a well known experience that for sparse structures one can find fast algorithm for some proble...
In many situations, a solution to a practical problem is sparse, i.e., corresponds to the case when ...
A vector or matrix is said to be sparse if the number of non-zero elements is significantly smaller ...
The aim of this work package (WP) is to explore approaches to learn structured sparse models, that i...
Sparse estimation methods are aimed at using or obtaining parsimo-nious representations of data or m...
This paper proposes a model with boundedly rational features in which the decision-maker (DM) builds...
International audienceSolving zero-sum matrix games is polynomial, because it boils down to linear p...
The aim of this thesis is to conduct a general investigation in the field of sparse matrices, to inv...
Arguably one of the most notable forms of the principle of parsimony was formulated by the philosoph...
This paper aims to provide a sharp excess risk guarantee for learning a sparse linear model without ...
2018-08-13This dissertation contains three individual collaborative studies for sparse learning prob...
We introduce a framework for sparsity structures defined via graphs. Our approach is flexible and ge...
In the last decade, the demand for statistical and computation methods for data analysis that involv...
The pioneering work on parameter orthogonalization by Cox and Reid (1987) is presented as an inducem...
We introduce a framework for sparsity structures defined via graphs. Our approach is flexible and ge...