The conjugate gradient (CG) method is the most widely used iterative scheme forthe solution of large sparse systems of linear equations when the matrix is symmetric positivedefinite. Although more than sixty year old, it is still a serious candidate for extreme-scalecomputation on large computing platforms. On the technological side, the continuous shrinkingof transistor geometry and the increasing complexity of these devices affect dramatically theirsensitivity to natural radiation, and thus diminish their reliability. One of the most common effectsproduced by natural radiation is the single event upset which consists in a bit-flip in a memory cellproducing unexpected results at application level. Consequently, the future computing facili...
Cette thèse étudie la méthode du gradient conjugué et la méthode de Lanczos pour la résolution de pr...
Satellite image processing is considered one of the more interesting areas in the fields of digital ...
The impressive breakthroughs of the last two decades in the field of machine learning can be in larg...
The conjugate gradient (CG) method is the most widely used iterative scheme forthe solution of large...
The main objective of this thesis is to develop techniques that can beused to analyze and mitigate t...
In this thesis, spatial multiplexing-MIMO communication schemes with OFDM modulation are considered ...
With rapid development of mathematical models and simulation tools, the need of uncertainty quantifi...
High performance computing applications must be resilient to faults, which are common occurrences es...
In the regression framework, many studies are focused on the high-dimensional problem where the numb...
Dynamic voltage scaling (DVS) technique is primarily used in digital design to enhance the energy ef...
Many methods are available to detect silent errors in high-performancecomputing (HPC) applications. ...
Comprehensive characterization of DNA variations can help to progress in multiple cancer genomics fi...
Recently, a lot of algorithms minimizing a non-convex energy function have been proposed to salve l...
International audienceOn s'intéresse à la résolution numérique du problème de déconvolution sans gri...
Derivative-free optimization (DFO) has enjoyed renewed interest over the past years, mostly motivate...
Cette thèse étudie la méthode du gradient conjugué et la méthode de Lanczos pour la résolution de pr...
Satellite image processing is considered one of the more interesting areas in the fields of digital ...
The impressive breakthroughs of the last two decades in the field of machine learning can be in larg...
The conjugate gradient (CG) method is the most widely used iterative scheme forthe solution of large...
The main objective of this thesis is to develop techniques that can beused to analyze and mitigate t...
In this thesis, spatial multiplexing-MIMO communication schemes with OFDM modulation are considered ...
With rapid development of mathematical models and simulation tools, the need of uncertainty quantifi...
High performance computing applications must be resilient to faults, which are common occurrences es...
In the regression framework, many studies are focused on the high-dimensional problem where the numb...
Dynamic voltage scaling (DVS) technique is primarily used in digital design to enhance the energy ef...
Many methods are available to detect silent errors in high-performancecomputing (HPC) applications. ...
Comprehensive characterization of DNA variations can help to progress in multiple cancer genomics fi...
Recently, a lot of algorithms minimizing a non-convex energy function have been proposed to salve l...
International audienceOn s'intéresse à la résolution numérique du problème de déconvolution sans gri...
Derivative-free optimization (DFO) has enjoyed renewed interest over the past years, mostly motivate...
Cette thèse étudie la méthode du gradient conjugué et la méthode de Lanczos pour la résolution de pr...
Satellite image processing is considered one of the more interesting areas in the fields of digital ...
The impressive breakthroughs of the last two decades in the field of machine learning can be in larg...