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2020 – today
- 2024
[j57]Aleksei Sholokhov, James V. Burke, Damian F. Santomauro, Peng Zheng, Aleksandr Y. Aravkin
:
A Relaxation Approach to Feature Selection for Linear Mixed Effects Models. J. Comput. Graph. Stat. 33(1): 261-275 (2024)- 2021
- [j56]James V. Burke, Tim Hoheisel, Quang V. Nguyen:
A Study of Convex Convex-Composite Functions via Infimal Convolution with Applications. Math. Oper. Res. 46(4): 1324-1348 (2021) - [j55]Aleksandr Y. Aravkin, James V. Burke, Daiwei He:
On the Global Minimizers of Real Robust Phase Retrieval With Sparse Noise. IEEE Trans. Inf. Theory 67(3): 1886-1896 (2021) - 2020
- [j54]James V. Burke, Abraham Engle:
Strong Metric (Sub)regularity of Karush-Kuhn-Tucker Mappings for Piecewise Linear-Quadratic Convex-Composite Optimization and the Quadratic Convergence of Newton's Method. Math. Oper. Res. 45(3): 1164-1192 (2020) - [j53]James V. Burke, Xiaojun Chen, Hailin Sun:
The subdifferential of measurable composite max integrands and smoothing approximation. Math. Program. 181(2): 229-264 (2020) - [j52]James V. Burke, Frank E. Curtis, Hao Wang, Jiashan Wang:
Inexact Sequential Quadratic Optimization with Penalty Parameter Updates within the QP Solver. SIAM J. Optim. 30(3): 1822-1849 (2020)
2010 – 2019
- 2019
- [j51]Jonathan Jonker, Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto, Sarah E. Webster:
Fast robust methods for singular state-space models. Autom. 105: 399-405 (2019) - [j50]Aleksandr Y. Aravkin, James V. Burke, Dmitriy Drusvyatskiy, Michael P. Friedlander, Scott Roy:
Level-set methods for convex optimization. Math. Program. 174(1-2): 359-390 (2019) - [j49]James V. Burke, Yuan Gao, Tim Hoheisel:
Variational Properties of Matrix Functions via the Generalized Matrix-Fractional Function. SIAM J. Optim. 29(3): 1958-1987 (2019) - [c5]Jonathan Jonker, Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto, Sarah E. Webster:
Robust Singular Smoothers for Tracking Using Low-Fidelity Data. Robotics: Science and Systems 2019 - [i4]Aleksandr Y. Aravkin, James V. Burke, Daiwei He:
On the Global Minimizers of Real Robust Phase Retrieval with Sparse Noise. CoRR abs/1905.10358 (2019) - 2018
- [j48]James V. Burke, Julia Eaton:
Variational analysis of convexly generated spectral max functions. Math. Program. 168(1-2): 63-92 (2018) - [j47]James V. Burke, Yuan Gao, Tim Hoheisel:
Convex Geometry of the Generalized Matrix-Fractional Function. SIAM J. Optim. 28(3): 2189-2200 (2018) - [j46]Aleksandr Y. Aravkin, James V. Burke, Dmitriy Drusvyatskiy, Michael P. Friedlander, Kellie J. MacPhee:
Foundations of Gauge and Perspective Duality. SIAM J. Optim. 28(3): 2406-2434 (2018) - [j45]Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto:
Generalized System Identification with Stable Spline Kernels. SIAM J. Sci. Comput. 40(5): B1419-B1443 (2018) - 2017
- [j44]Aleksandr Y. Aravkin, James V. Burke, Lennart Ljung, Aurélie C. Lozano, Gianluigi Pillonetto:
Generalized Kalman smoothing: Modeling and algorithms. Autom. 86: 63-86 (2017) - [i3]James V. Burke, Yuan Gao, Tim Hoheisel:
Convex Geometry of the Generalized Matrix-Fractional Function. CoRR abs/1703.01363 (2017) - 2016
- [i2]Aleksandr Y. Aravkin, James V. Burke, Dmitriy Drusvyatskiy, Michael P. Friedlander, Scott Roy:
Level-set methods for convex optimization. CoRR abs/1602.01506 (2016) - 2015
- [j43]James V. Burke, Frank E. Curtis, Hao Wang, Jiashan Wang:
Iterative Reweighted Linear Least Squares for Exact Penalty Subproblems on Product Sets. SIAM J. Optim. 25(1): 261-294 (2015) - [j42]James V. Burke, Tim Hoheisel:
Matrix Support Functionals for Inverse Problems, Regularization, and Learning. SIAM J. Optim. 25(2): 1135-1159 (2015) - [j41]Aleksandr Y. Aravkin, Bradley M. Bell, James V. Burke, Gianluigi Pillonetto:
The Connection Between Bayesian Estimation of a Gaussian Random Field and RKHS. IEEE Trans. Neural Networks Learn. Syst. 26(7): 1518-1524 (2015) - 2014
- [j40]Aleksandr Y. Aravkin, James V. Burke, Alessandro Chiuso, Gianluigi Pillonetto:
Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso. J. Mach. Learn. Res. 15(1): 217-252 (2014) - [j39]Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto:
Robust and Trend-Following Student's t Kalman Smoothers. SIAM J. Control. Optim. 52(5): 2891-2916 (2014) - [j38]James V. Burke, Frank E. Curtis, Hao Wang:
A Sequential Quadratic Optimization Algorithm with Rapid Infeasibility Detection. SIAM J. Optim. 24(2): 839-872 (2014) - [c4]Aleksandr Y. Aravkin, James V. Burke:
Smoothing dynamic systems with state-dependent covariance matrices. CDC 2014: 3382-3387 - 2013
- [j37]Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto:
Sparse/robust estimation and Kalman smoothing with nonsmooth log-concave densities: modeling, computation, and theory. J. Mach. Learn. Res. 14(1): 2689-2728 (2013) - [j36]James V. Burke, Tim Hoheisel:
Epi-convergent Smoothing with Applications to Convex Composite Functions. SIAM J. Optim. 23(3): 1457-1479 (2013) - [j35]Aleksandr Y. Aravkin, James V. Burke, Michael P. Friedlander:
Variational Properties of Value Functions. SIAM J. Optim. 23(3): 1689-1717 (2013) - [c3]Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto:
Linear system identification using stable spline kernels and PLQ penalties. CDC 2013: 5168-5173 - [i1]Aleksandr Y. Aravkin, Bradley M. Bell, James V. Burke, Gianluigi Pillonetto:
The exact relationship between regularization in RKHS and Bayesian estimation of Gaussian random fields. CoRR abs/1301.5288 (2013) - 2012
- [c2]Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto:
Nonsmooth regression and state estimation using piecewise quadratic log-concave densities. CDC 2012: 4101-4106 - 2011
- [j34]Aleksandr Y. Aravkin, Bradley M. Bell, James V. Burke, Gianluigi Pillonetto:
An 1 -Laplace Robust Kalman Smoother. IEEE Trans. Autom. Control. 56(12): 2898-2911 (2011) - [c1]Aleksandr Y. Aravkin, James V. Burke, Alessandro Chiuso, Gianluigi Pillonetto:
Convex vs nonconvex approaches for sparse estimation: Lasso, Multiple Kernel Learning and Hyperparameter Lasso. CDC/ECC 2011: 156-161
2000 – 2009
- 2009
- [j33]Bradley M. Bell, James V. Burke, Gianluigi Pillonetto:
An inequality constrained nonlinear Kalman-Bucy smoother by interior point likelihood maximization. Autom. 45(1): 25-33 (2009) - [j32]James V. Burke, Sien Deng:
Weak sharp minima revisited, Part III: error bounds for differentiable convex inclusions. Math. Program. 116(1-2): 37-56 (2009) - 2007
- [j31]James V. Burke, Adrian S. Lewis, Michael L. Overton:
Spectral conditioning and pseudospectral growth. Numerische Mathematik 107(1): 27-37 (2007) - [j30]James V. Burke, Adrian S. Lewis, Michael L. Overton:
Convexity and Lipschitz Behavior of Small Pseudospectra. SIAM J. Matrix Anal. Appl. 29(2): 586-595 (2007) - 2006
- [j29]James V. Burke, Didier Henrion, Adrian S. Lewis, Michael L. Overton:
Stabilization via Nonsmooth, Nonconvex Optimization. IEEE Trans. Autom. Control. 51(11): 1760-1769 (2006) - 2005
- [j28]James V. Burke, Sien Deng:
Weak sharp minima revisited, part II: application to linear regularity and error bounds. Math. Program. 104(2-3): 235-261 (2005) - [j27]James V. Burke, Adrian S. Lewis, Michael L. Overton:
Variational analysis of functions of the roots of polynomials. Math. Program. 104(2-3): 263-292 (2005) - [j26]James V. Burke, Adrian S. Lewis, Michael L. Overton:
A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization. SIAM J. Optim. 15(3): 751-779 (2005) - 2004
- [j25]James V. Burke, Adrian S. Lewis, Michael L. Overton:
Variational Analysis of the Abscissa Mapping for Polynomials via the Gauss-Lucas Theorem. J. Glob. Optim. 28(3-4): 259-268 (2004) - [j24]James V. Burke, Adrian S. Lewis, Michael L. Overton:
Pseudospectral Components and the Distance to Uncontrollability. SIAM J. Matrix Anal. Appl. 26(2): 350-361 (2004) - 2003
- [j23]James V. Burke, D. Russell Luke:
Variational Analysis Applied to the Problem of Optical Phase Retrieval. SIAM J. Control. Optim. 42(2): 576-595 (2003) - [j22]James V. Burke, Adrian S. Lewis, Michael L. Overton:
Optimization and Pseudospectra, with Applications to Robust Stability. SIAM J. Matrix Anal. Appl. 25(1): 80-104 (2003) - 2002
- [j21]James V. Burke, Adrian S. Lewis, Michael L. Overton:
Approximating Subdifferentials by Random Sampling of Gradients. Math. Oper. Res. 27(3): 567-584 (2002) - [j20]D. Russell Luke, James V. Burke, Richard G. Lyon:
Optical Wavefront Reconstruction: Theory and Numerical Methods. SIAM Rev. 44(2): 169-224 (2002) - 2001
- [j19]James V. Burke, Adrian S. Lewis, Michael L. Overton:
Optimal Stability and Eigenvalue Multiplicity. Found. Comput. Math. 1(2): 205-225 (2001) - [j18]James V. Burke, Michael L. Overton:
Variational analysis of non-Lipschitz spectral functions. Math. Program. 90(2): 317-351 (2001) - [j17]James V. Burke, Michael L. Overton:
Variational Analysis of the Abscissa Mapping for Polynomials. SIAM J. Control. Optim. 39(6): 1651-1676 (2001) - 2000
- [j16]James V. Burke, Song Xu:
A non-interior predictor-corrector path following algorithm for the monotone linear complementarity problem. Math. Program. 87(1): 113-130 (2000) - [j15]James V. Burke, Maijian Qian:
On the superlinear convergence of the variable metric proximal point algorithm using Broyden and BFGS matrix secant updating. Math. Program. 88(1): 157-181 (2000)
1990 – 1999
- 1999
- [j14]Song Xu, James V. Burke:
A polynomial time interior-point path-following algorithm for LCP based on Chen-Harker-Kanzow smoothing techniques. Math. Program. 86(1): 91-103 (1999) - 1998
- [j13]James V. Burke, Song Xu:
The Global Linear Convergence of a Noninterior Path-Following Algorithm for Linear Complementarity Problems. Math. Oper. Res. 23(3): 719-734 (1998) - 1997
- [j12]Julio Moro, James V. Burke, Michael L. Overton:
On the Lidskii-Vishik-Lyusternik Perturbation Theory for Eigenvalues of Matrices with Arbitrary Jordan Structure. SIAM J. Matrix Anal. Appl. 18(4): 793-817 (1997) - 1996
- [j11]James V. Burke, Paul Tseng:
A Unified Analysis of Hoffman's Bound via Fenchel Duality. SIAM J. Optim. 6(2): 265-282 (1996) - 1995
- [j10]James V. Burke, Michael C. Ferris:
A Gauss-Newton method for convex composite optimization. Math. Program. 71: 179-194 (1995) - 1994
- [j9]James V. Burke, Jorge J. Moré:
Exposing Constraints. SIAM J. Optim. 4(3): 573-595 (1994) - 1992
- [j8]James V. Burke, R. A. Poliquin:
Optimality conditions for non-finite valued convex composite functions. Math. Program. 57: 103-120 (1992) - [j7]James V. Burke:
A Robust Trust Region Method for Constrained Nonlinear Programming Problems. SIAM J. Optim. 2(2): 325-347 (1992) - 1991
- [j6]James V. Burke, Michael C. Ferris:
Characterization of solution sets of convex programs. Oper. Res. Lett. 10(1): 57-60 (1991) - 1990
- [j5]James V. Burke, Jorge J. Moré, Gerardo Toraldo:
Convergence Properties of Trust Region Methods for Linear and Convex Constraints. Math. Program. 47: 305-336 (1990)
1980 – 1989
- 1989
- [j4]James V. Burke, Shih-Ping Han:
A robust sequential quadratic programming method. Math. Program. 43(1-3): 277-303 (1989) - 1987
- [j3]James V. Burke:
Second order necessary and sufficient conditions for convex composite NDO. Math. Program. 38(3): 287-302 (1987) - 1986
- [j2]James V. Burke, S.-P. Han:
A Gauss-Newton Approach to Solving Generalized Inequalities. Math. Oper. Res. 11(4): 632-643 (1986) - 1985
- [j1]James V. Burke:
Descent methods for composite nondifferentiable optimization problems. Math. Program. 33(3): 260-279 (1985)
Coauthor Index
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