An efficient and robust numerical algorithm for estimating parameters in Turing systems
Journal of Computational Physics
Semismooth Newton Methods for Time-Optimal Control for a Class of ODEs
SIAM Journal on Control and Optimization
SIAM Journal on Optimization
SIAM Journal on Scientific Computing
Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems
SIAM Journal on Scientific Computing
SIAM Journal on Imaging Sciences
SIAM Journal on Scientific Computing
Image-Driven Parameter Estimation in Absorption-Diffusion Models of Chromoscopy
SIAM Journal on Imaging Sciences
Minimal Effort Problems and Their Treatment by Semismooth Newton Methods
SIAM Journal on Control and Optimization
Karush-Kuhn-Tucker Conditions for Nonsmooth Mathematical Programming Problems in Function Spaces
SIAM Journal on Control and Optimization
Path-following for optimal control of stationary variational inequalities
Computational Optimization and Applications
Optimal control of Maxwell's equations with regularized state constraints
Computational Optimization and Applications
A measure space approach to optimal source placement
Computational Optimization and Applications
Convergence of distributed optimal control problems governed by elliptic variational inequalities
Computational Optimization and Applications
Bregmanized Domain Decomposition for Image Restoration
Journal of Scientific Computing
A two-stage method for inverse medium scattering
Journal of Computational Physics
Eulerian-on-lagrangian simulation
ACM Transactions on Graphics (TOG)
An extension of the Basic Constraint Qualification to nonconvex vector optimization problems
Journal of Global Optimization
A minimum effort optimal control problem for the wave equation
Computational Optimization and Applications
Computational Optimization and Applications
A Variational Framework for Region-Based Segmentation Incorporating Physical Noise Models
Journal of Mathematical Imaging and Vision
Efficient numerical schemes for viscoplastic avalanches. Part 1: The 1D case
Journal of Computational Physics
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Lagrange multiplier theory provides a tool for the analysis of a general class of nonlinear variational problems and is the basis for developing efficient and powerful iterative methods for solving these problems. This comprehensive monograph analyzes Lagrange multiplier theory and shows its impact on the development of numerical algorithms for problems posed in a function space setting. The book is motivated by the idea that a full treatment of a variational problem in function spaces would not be complete without a discussion of infinite-dimensional analysis, proper discretization, and the relationship between the two. The authors develop and analyze efficient algorithms for constrained optimization and convex optimization problems based on the augumented Lagrangian concept and cover such topics as sensitivity analysis, convex optimization, second order methods, and shape sensitivity calculus. General theory is applied to challenging problems in optimal control of partial differential equations, image analysis, mechanical contact and friction problems, and American options for the Black-Scholes model. Audience: This book is for researchers in optimization and control theory, numerical PDEs, and applied analysis. It will also be of interest to advanced graduate students in applied analysis and PDE optimization.