Faculty

Institution

Unit

Venue

James V. Burke

Professor Emeritus

University of Washington

Affiliations

College of Arts & Sciences

Department of Mathematics

James V. Burke is currently serving as a College Of Arts & Sciences Professor Emeritus at University of Washington.

Honors

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Experiences

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Education

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James V. Burke

University of Washington

Professor Emeritus

You can click a keyword to filter the displayed items

applied mathematics mathematical optimization machine learning mathematical programming computational optimization mathematics computer science numerical analysis systems engineering control optimization automatic control spectral theory variational analysis approximation theory numerical linear algebra linear algebra numerical algorithms convex analysis matrix analysis control systems dynamical systems functional analysis mathematical control theory control science convex optimization estimation theory nondifferentiable optimization matrix theory engineering optimization robust optimization numerical stability unconstrained optimization continuous optimization stability analysis state estimation robust control weak sharp minima numerical mathematics global optimization nonlinear analysis derivative-free optimization constrained optimization sparse estimation block tridiagonal systems generalized matrix-fractional function control system technology mathematical sciences quadratic programming automated machine learning linear programming optimization problems nonlinear programming orthogonal polynomials kernel methods stabilization technique regularization (mathematics) nonconvex optimization numerical methods monotone operators variational properties abscissa mapping infimal convolution exact penalty subproblems stable spline kernels cybernetics machine learning research control design control engineering model-based control technique optical engineering matrix methods analytic number theory termination criteria gradient sampling rare decays rate asymmetries quadratic convergence

Notable Scholarly Works

Notable Federal Grants

Collaborators

On the Subdifferentiability of Functions of a Matrix Spectrum I: Mathematical Foundations. Routledge eBooks (2024)

Michael OvertonJames V. Burke

mathematical optimizationspectral analysisfunctional analysisspectrum estimationtheoretical analysismathematical foundationsnumerical analysislinear algebramatrix theorymathematicsmatrix analysisspectral theorynumerical linear algebrafunction spacesapproximation theoryapplied mathematics

A Relaxation Approach to Feature Selection for Linear Mixed Effects Models. Journal of Computational and Graphical Statistics (2023)

Aleksei SholokhovJames V. BurkeDamian F. SantomauroPeng ZhengAleksandr Aravkin

relaxation processfeature selectionstatistical inferencestatistics

Algorithms for Block Tridiagonal Systems: Stability Results for Generalized Kalman Smoothing. IFAC-PapersOnLine (2021)

Aleksandr Y. AravkinJames V. BurkeBradley M. BellGianluigi Pillonetto

computer sciencestabilityblock tridiagonal systems

A Study of Convex Convex-Composite Functions via Infimal Convolution With Applications. Mathematics of Operations Research (2021)

James V. BurkeHoheisel TimQuang V. Nguyen

mathematical optimizationoperations researchconvex convex-composite functionsmathematicsinfimal convolutionapplied mathematics

Convergence of the Gradient Sampling Algorithm on Directionally Lipschitz Functions. Set-Valued and Variational Analysis (2021)

J. V. BurkeQ. Lin

numerical analysisvariational analysismathematical analysisapplied mathematicsmachine learning

An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution With Applications to Population Pharmacokinetics. Pharmaceutics (2020)

Walter M. YamadaMichael N. NeelyJay BartroffDavid S. BayardJames V. BurkeMike van GuilderRoger W. JelliffeAlona KryshchenkoRobert LearyTatiana TatarinovaAlan Schumitzky

nonparametric estimationpharmacokineticspharmacologyapplied statisticsapplied mathematicsstatistics

Gradient Sampling Methods for Nonsmooth Optimization. Springer eBooks (2020)

James V. BurkeFrank E. CurtisAdrian S. LewisMichael L. OvertonLucas E. A. Simões

mathematical optimizationnondifferentiable optimizationgradient sampling methodsderivative-free optimizationunconstrained optimizationnumerical analysismathematical programmingcomputational optimizationconvex analysisapproximation theoryapplied mathematics

Inexact Sequential Quadratic Optimization With Penalty Parameter Updates Within the Qp Solver. SIAM Journal on Optimization (2020)

James V. BurkeFrank E. CurtisHao WangJiashan Wang

mathematical optimizationcomputer sciencecomputational optimizationqp solvermachine learningpenalty parameter updates

On the Global Minimizers of Real Robust Phase Retrieval With Sparse Noise. IEEE Transactions on Information Theory (2020)

Aleksandr AravkinJames V. BurkeDaiwei He

information theorycomputer sciencephase retrievalsparse noiseelectrical engineeringglobal minimizersdigital signal processingapplied mathematicsinformation technology

Strong Metric (Sub)Regularity of Karush–Kuhn–Tucker Mappings for Piecewise Linear-Quadratic Convex-Composite Optimization and the Quadratic Convergence of Newton’s Method. Mathematics of Operations Research (2020)

James V. BurkeAbraham Engle

mathematical optimizationoperations researchkarush-kuhn-tucker mappingsquadratic convergenceapplied mathematics

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James V. Burke

Professor Emeritus

University of Washington

Affiliations

College of Arts & Sciences

Department of Mathematics

James V. Burke is currently serving as a College Of Arts & Sciences Professor Emeritus at University of Washington.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

James V. Burke

University of Washington

Professor Emeritus

You can click a keyword to filter the displayed items

Notable Scholarly Works

Notable Federal Grants

Collaborators

On the Subdifferentiability of Functions of a Matrix Spectrum I: Mathematical Foundations. Routledge eBooks (2024)

Michael OvertonJames V. Burke

A Relaxation Approach to Feature Selection for Linear Mixed Effects Models. Journal of Computational and Graphical Statistics (2023)

Aleksei SholokhovJames V. BurkeDamian F. SantomauroPeng ZhengAleksandr Aravkin

relaxation processfeature selectionstatistical inferencestatistics

Algorithms for Block Tridiagonal Systems: Stability Results for Generalized Kalman Smoothing. IFAC-PapersOnLine (2021)

Aleksandr Y. AravkinJames V. BurkeBradley M. BellGianluigi Pillonetto

computer sciencestabilityblock tridiagonal systems

A Study of Convex Convex-Composite Functions via Infimal Convolution With Applications. Mathematics of Operations Research (2021)

James V. BurkeHoheisel TimQuang V. Nguyen

mathematical optimizationoperations researchconvex convex-composite functionsmathematicsinfimal convolutionapplied mathematics

Convergence of the Gradient Sampling Algorithm on Directionally Lipschitz Functions. Set-Valued and Variational Analysis (2021)

J. V. BurkeQ. Lin

numerical analysisvariational analysismathematical analysisapplied mathematicsmachine learning

An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution With Applications to Population Pharmacokinetics. Pharmaceutics (2020)

Walter M. YamadaMichael N. NeelyJay BartroffDavid S. BayardJames V. BurkeMike van GuilderRoger W. JelliffeAlona KryshchenkoRobert LearyTatiana TatarinovaAlan Schumitzky

nonparametric estimationpharmacokineticspharmacologyapplied statisticsapplied mathematicsstatistics

Gradient Sampling Methods for Nonsmooth Optimization. Springer eBooks (2020)

James V. BurkeFrank E. CurtisAdrian S. LewisMichael L. OvertonLucas E. A. Simões

Inexact Sequential Quadratic Optimization With Penalty Parameter Updates Within the Qp Solver. SIAM Journal on Optimization (2020)

James V. BurkeFrank E. CurtisHao WangJiashan Wang

mathematical optimizationcomputer sciencecomputational optimizationqp solvermachine learningpenalty parameter updates

On the Global Minimizers of Real Robust Phase Retrieval With Sparse Noise. IEEE Transactions on Information Theory (2020)

Aleksandr AravkinJames V. BurkeDaiwei He

information theorycomputer sciencephase retrievalsparse noiseelectrical engineeringglobal minimizersdigital signal processingapplied mathematicsinformation technology

James V. BurkeAbraham Engle

mathematical optimizationoperations researchkarush-kuhn-tucker mappingsquadratic convergenceapplied mathematics

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Similar Faculty Members

Lieven Vandenberghe

Institution

University of California, Los Angeles

engineering optimization

continuous optimization

Dmitriy Drusvyatskiy

Institution

University of Washington

continuous optimization

design optimization

Shuzhong Zhang

Institution

University of Minnesota

operations research

empirical algorithmics

Michael L. Overton

Institution

New York University

mathematical optimization

semi-definite optimization

Kazufumi Ito

Institution

North Carolina State University

applied mathematical modelling

pde-constrained optimization

Pablo A. Parrilo

Institution

Massachusetts Institute of Technology

numerical linear algebra

semidefinite optimization

R. Tyrrell Rockafellar

Institution

University of Washington

engineering optimization

continuous optimization

Patrick L. Combettes

Institution

North Carolina State University

convex analysis

inverse problems

Stephen Wright

Institution

University of Wisconsin–Madison

continuous optimization

linear programming

Gang Yin

Institution

Wayne State University

applied mathematics

systems engineering