Regularization and interior point approaches offer valuable perspectives to address constrained nonlinear optimization problems in view of control applications. This paper discusses the interactions between these tech...
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Regularization and interior point approaches offer valuable perspectives to address constrained nonlinear optimization problems in view of control applications. This paper discusses the interactions between these techniques and proposes an algorithm that synergistically combines them. Building a sequence of closely related subproblems and approximately solving each of them, this approach inherently exploits warm-starting, early termination, and the possibility to adopt subsolvers tailored to specific problem structures. Moreover, by relaxing the equality constraints with a proximal penalty, the regularized subproblems are feasible and satisfy a strong constraint qualification by construction, allowing the safe use of efficient solvers. We show how regularization benefits the underlying linear algebra and a detailed convergence analysis indicates that limit points tend to minimize constraint violation and satisfy suitable optimality conditions. Finally, numerical results indicate that the combined approach compares favorably, in terms of robustness, against both interior point and augmented Lagrangian codes.
Over the last decade, the combination of compressed sensing (CS) with acquisition over multiple receiver coils in magnetic resonance imaging (MRI) has allowed the emergence of faster scans while maintaining a good sig...
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Over the last decade, the combination of compressed sensing (CS) with acquisition over multiple receiver coils in magnetic resonance imaging (MRI) has allowed the emergence of faster scans while maintaining a good signal-to-noise ratio (SNR). Self-calibrating techniques, such as ESPiRIT, have become the standard approach to estimating the coil sensitivity maps prior to the reconstruction stage. In this work, we proceed differently and introduce a new calibration-less multi-coil CS reconstruction method. Calibration-less techniques no longer require the prior extraction of sensitivity maps to perform multi-coil image reconstruction but usually alternate estimation sensitivity map estimation and image reconstruction. Here, to get rid of the nonconvexity of the latter approach we reconstruct as many MR images as the number of coils. To compensate for the ill-posedness of this inverse problem, we leverage structured sparsity of the multi-coil images in a wavelet transform domain while adapting to variations in SNR across coils owing to the OSCAR (octagonal shrinkage and clustering algorithm for regression) regularization. Coil-specific complex-valued MR images are thus obtained by minimizing a convex but nonsmooth objective function using the proximal primal-dual Condat-Vu algorithm. Comparison and validation on retrospective Cartesian and non-Cartesian studies based on the Brain fastMRI data set demonstrate that the proposed reconstruction method outperforms the state-of-the-art (l(1)-ESPIRiT, calibration-less AC-LORAKS and CaLM methods) significantly on magnitude images for the T1 and FLAIR contrasts. Additionally, further validation operated on 8 to 20-fold prospectively accelerated high-resolution ex vivo human brain MRI data collected at 7 Tesla confirms the retrospective results. Overall, OSCAR-based regularization preserves phase information more accurately (both visually and quantitatively) compared to other approaches, an asset that can only be assessed on real
Many modern optimization problems admit a composite form in which the objective function is given by the sum of a smooth term and a nonsmooth regularizer. Such problems can be solved via proximal methods and their var...
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Many modern optimization problems admit a composite form in which the objective function is given by the sum of a smooth term and a nonsmooth regularizer. Such problems can be solved via proximal methods and their variants, including the Douglas-Rachford (DR) splitting algorithm. In this paper, we view the DR splitting flow as a dynamical system and leverage techniques from control theory to study its global stability properties. In particular, for problems with strongly convex objective functions, we utilize the theory of integral quadratic constraints to prove global exponential stability of the ordinary differential equation that governs the evolution of the DR splitting flow. In our analysis, we use the fact that this algorithm can be interpreted as a variable-metric gradient method on the DR envelope and exploit structural properties of nonlinear terms that arise from composition of reflected proximal operators.
The Empirical Mode Decomposition (EMD) is known to be a powerful tool adapted to the decomposition of a signal into a collection of intrinsic mode functions (IMF). A key procedure in the extraction of the IMFs is the ...
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ISBN:
(纸本)9781467310680
The Empirical Mode Decomposition (EMD) is known to be a powerful tool adapted to the decomposition of a signal into a collection of intrinsic mode functions (IMF). A key procedure in the extraction of the IMFs is the sifting process whose main drawback is to depend on the choice of an interpolation method and to have no clear convergence guarantees. We propose a convex optimization procedure in order to replace the sifting process in the EMD. The considered method is based on proximal tools, which allow us to deal with a large class of constraints such as quasi-orthogonality or extremabased constraints.
This paper concerns the study of anon-smoothlogistic regression function. The focus is on a high-dimensional binary response case by penalizing the decomposition of the unknown logit regression function on a wavelet b...
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This paper concerns the study of anon-smoothlogistic regression function. The focus is on a high-dimensional binary response case by penalizing the decomposition of the unknown logit regression function on a wavelet basis of functions evaluated on the sampling design. Sample sizes are arbitrary (not necessarily dyadic) and we consider general designs. We study separable wavelet estimators, exploiting sparsity of wavelet decompositions for signals belonging to homogeneous Besov spaces, and using efficient iterative proximal gradient descent algorithms. We also discuss a level by level block wavelet penalization technique, leading to a type of regularization in multiple logistic regression with grouped predictors. Theoretical and numerical properties of the proposed estimators are investigated. A simulation study examines the empirical performance of the proposed procedures, and real data applications demonstrate their effectiveness.
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