P. Combettes

发表

The use of noise properties in set theoretic estimation

Patrick L. Combettes, H. Joel Trussell, H. Trussell, 1991, IEEE Trans. Signal Process..

The foundations of set theoretic estimation

Patrick L. Combettes, Mehmet Reha Civanlar, P. L. Combettes, 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

Convex Analysis and Monotone Operator Theory in Hilbert Spaces

Heinz H. Bauschke, Patrick L. Combettes, P. L. Combettes, 2011, CMS Books in Mathematics.

The Baillon-Haddad Theorem Revisited

Heinz H. Bauschke, P. L. Combettes, P. Combettes, 2009, 0906.0807.

Hybrid projection-reflection method for phase retrieval.

Heinz H. Bauschke, Heinz H Bauschke, Patrick L Combettes, 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

Hilbertian convex feasibility problem: Convergence of projection methods

P. L. Combettes, P. Combettes, 1997 .

Convex set theoretic image recovery by extrapolated iterations of parallel subgradient projections

Patrick L. Combettes, P. L. Combettes, P. Combettes, 1997, IEEE Trans. Image Process..

On the effectiveness of projection methods for convex feasibility problems with linear inequality constraints

Patrick L. Combettes, Wei Chen, Ran Davidi, 2009, Computational Optimization and Applications.

Journal of Mathematical Analysis and Applications

V. G. Troitsky, P. L. Combettes, L. Henlein, 1960 .

Generalized Mann iterates for constructing fixed points in Hilbert spaces

P. L. Combettes, T. Pennanen, P. Combettes, 2002 .

Proximal Methods for Cohypomonotone Operators

Patrick L. Combettes, Teemu Pennanen, P. L. Combettes, 2004, SIAM J. Control. Optim..

Quasi-Fejérian Analysis of Some Optimization Algorithms

P. L. Combettes, P. Combettes, 2001 .

Fixed Point Strategies in Data Science

P. L. Combettes, J. Pesquet, P. Combettes, 2020, IEEE Transactions on Signal Processing.

Proximal Activation of Smooth Functions in Splitting Algorithms for Convex Image Recovery

Patrick L. Combettes, Lilian E. Glaudin, P. L. Combettes, 2018, SIAM J. Imaging Sci..

Asynchronous block-iterative primal-dual decomposition methods for monotone inclusions

Patrick L. Combettes, Jonathan Eckstein, Jonathan Eckstein, 2015, Mathematical Programming.

A forward-backward view of some primal-dual optimization methods in image recovery

Patrick L. Combettes, Bang Công Vu, Jean-Christophe Pesquet, 2014, 2014 IEEE International Conference on Image Processing (ICIP).

Solving Coupled Composite Monotone Inclusions by Successive Fejér Approximations of their Kuhn-Tucker Set

Patrick L. Combettes, Naseer Shahzad, Abdullah Alotaibi, 2013, SIAM J. Optim..

Primal-Dual Splitting Algorithm for Solving Inclusions with Mixtures of Composite, Lipschitzian, and Parallel-Sum Type Monotone Operators

P. L. Combettes, J. Pesquet, P. Combettes, 2011, Set-Valued and Variational Analysis.

Proximal Splitting Methods in Signal Processing

Patrick L. Combettes, Jean-Christophe Pesquet, P. L. Combettes, 2009, Fixed-Point Algorithms for Inverse Problems in Science and Engineering.

Signal Recovery by Proximal Forward-Backward Splitting

Patrick L. Combettes, Valérie R. Wajs, P. L. Combettes, 2005, Multiscale Model. Simul..

Solving monotone inclusions via compositions of nonexpansive averaged operators

P. L. Combettes, P. Combettes, 2004 .

A Douglas–Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery

J. Pesquet, P. Combettes, 2007, IEEE Journal of Selected Topics in Signal Processing.

Joint minimization with alternating Bregman proximity operators

Heinz H. Bauschke, P. L. Combettes, D. Noll, 2005 .

Compositions and convex combinations of averaged nonexpansive operators

P. L. Combettes, I. Yamada, P. Combettes, 2014, 1407.5100.

A variational formulation for frame-based inverse problems

Valérie R. Wajs, P. L. Combettes, J. Pesquet, 2007 .

Dualization of Signal Recovery Problems

P. L. Combettes, B. Vu, Đ. Dũng, 2009, 0907.0436.

Functions with prescribed best linear approximations

Patrick L. Combettes, Noli N. Reyes, P. L. Combettes, 2009, J. Approx. Theory.

A proximal decomposition method for solving convex variational inverse problems

P. L. Combettes, J. Pesquet, P. Combettes, 2008, 0807.2617.

Convex Variational Formulation with Smooth Coupling for Multicomponent Signal Decomposition and Recovery

Luis, Briceno-Arias, Patrick, 2009 .

Method of successive projections for finding a common point of sets in metric spaces

H. Trussell, P. L. Combettes, P. Combettes, 1990 .

The Convex Feasibility Problem in Image Recovery

P. L. Combettes, P. Combettes, 1996 .

Regression Models for Compositional Data: General Log-Contrast Formulations, Proximal Optimization, and Microbiome Data Applications

Christian L. Müller, P. L. Combettes, P. Combettes, 2020, Statistics in Biosciences.

Regression Models for Compositional Data: General Log-Contrast Formulations, Proximal Optimization, and Microbiome Data Applications

Christian L. Müller, P. L. Combettes, P. Combettes, 2019, Statistics in Biosciences.

Monotone operator theory in convex optimization

P. L. Combettes, P. Combettes, 2018, Mathematical Programming.

Monotone operator theory in convex optimization

Patrick L. Combettes, P. L. Combettes, P. Combettes, 2018, Math. Program..

Consistent learning by composite proximal thresholding

Patrick L. Combettes, Saverio Salzo, Silvia Villa, 2015, Mathematical Programming.

Proximal Algorithms for Multicomponent Image Recovery Problems

Patrick L. Combettes, Nelly Pustelnik, Jean-Christophe Pesquet, 2011, Journal of Mathematical Imaging and Vision.

Split convex minimization algorithm for signal recovery

Patrick L. Combettes, Jean-Christophe Pesquet, J. Pesquet, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

ST ] 1 D ec 2 01 5 Consistent Learning by Composite Proximal Thresholding ∗

Patrick L. Combettes, Saverio Salzo, Silvia Villa, 2018 .

Some applications of proximal methods

P. L. Combettes, N. Pustelnik, L. Duval, 2013 .

Iterative construction of the resolvent of a sum of maximal monotone operators

P. L. Combettes, P. Combettes, 2009 .

2 J ul 2 00 9 Dualization of Signal Recovery Problems ∗

P. L. Combettes, B. Vu, D. Dung, 2022 .

Proximal Thresholding Algorithm for Minimization over Orthonormal Bases

Patrick L. Combettes, Jean-Christophe Pesquet, P. L. Combettes, 2007, SIAM J. Optim..

Extrapolation algorithm for affine-convex feasibility problems

Heinz H. Bauschke, Patrick L. Combettes, Serge G. Kruk, 2006, Numerical Algorithms.

Image restoration subject to a total variation constraint

Patrick L. Combettes, Jean-Christophe Pesquet, P. L. Combettes, 2004, IEEE Transactions on Image Processing.

Finding best approximation pairs relative to two closed convex sets in Hilbert spaces

Heinz H. Bauschke, Patrick L. Combettes, D. Russell Luke, 2004, J. Approx. Theory.

An adaptive level set method for nondifferentiable constrained image recovery

Patrick L. Combettes, Jian Luo, P. L. Combettes, 2002, IEEE Trans. Image Process..

Inconsistent signal feasibility problems: least-squares solutions in a product space

Patrick L. Combettes, P. L. Combettes, P. Combettes, 1994, IEEE Trans. Signal Process..

Iterating Bregman Retractions

Heinz H. Bauschke, Patrick L. Combettes, P. L. Combettes, 2002, SIAM J. Optim..

A Weak-to-Strong Convergence Principle for Fejé-Monotone Methods in Hilbert Spaces

Heinz H. Bauschke, Patrick L. Combettes, P. L. Combettes, 2001, Math. Oper. Res..

Fejér Monotonicity in Convex Optimization

Patrick L. Combettes, P. L. Combettes, P. Combettes, 2009, Encyclopedia of Optimization.

Stochastic forward-backward and primal-dual approximation algorithms with application to online image restoration

Patrick L. Combettes, Jean-Christophe Pesquet, P. L. Combettes, 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

Stochastic Approximations and Perturbations in Forward-Backward Splitting for Monotone Operators

P. L. Combettes, J. Pesquet, P. Combettes, 2015, 1507.07095.

Phase retrieval, error reduction algorithm, and Fienup variants: a view from convex optimization.

Heinz H. Bauschke, Heinz H Bauschke, Patrick L Combettes, 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

A subgradient projection algorithm for nondifferentiable signal recovery

Patrick L. Combettes, Jian Luo, Jianxue Luo, 1999, Nonlinear Signal and Image Processing.

A level-set subgradient projection algorithm for non-differentiable signal restoration with multiple constraints

Patrick L. Combettes, Jian Luo, Jianxue Luo, 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

Convex set theoretic image recovery with inexact projection algorithms

Patrick L. Combettes, P. Combettes, 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

Nonlinear Optimization: a bridge from theory to applications

J. Hiriart-Urruty, P. Combettes, Italy Erice, 2013 .

Bregman Monotone Optimization Algorithms

Heinz H. Bauschke, Jonathan M. Borwein, Patrick L. Combettes, 2003, SIAM J. Control. Optim..

ESSENTIAL SMOOTHNESS, ESSENTIAL STRICT CONVEXITY, AND LEGENDRE FUNCTIONS IN BANACH SPACES

Heinz H. Bauschke, J. Borwein, P. L. Combettes, 2001 .

Construction of best Bregman approximations in reflexive Banach spaces

Heinz H. Bauschke, P. L. Combettes, P. Combettes, 2003 .

Visco-penalization of the sum of two monotone operators

Patrick L. Combettes, Sever Adrian Hirstoaga, S. Hirstoaga, 2008 .