Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
A nonsmooth version of Newton's method
Mathematical Programming: Series A and B
Smoothing Methods and Semismooth Methods for Nondifferentiable Operator Equations
SIAM Journal on Numerical Analysis
The Primal-Dual Active Set Strategy as a Semismooth Newton Method
SIAM Journal on Optimization
The Primal-Dual Active Set Method for Nonlinear Optimal Control Problems with Bilateral Constraints
SIAM Journal on Control and Optimization
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Lagrange Multiplier Approach to Variational Problems and Applications
Lagrange Multiplier Approach to Variational Problems and Applications
SC1 optimization reformulations of the generalized Nash equilibrium problem
Optimization Methods & Software
SIAM Journal on Optimization
Computational Optimization and Applications
Electrical impedance tomography using level set representation and total variational regularization
Journal of Computational Physics
SIAM Journal on Optimization
A further result on an implicit function theorem for locally Lipschitz functions
Operations Research Letters
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In this paper we consider optimal control problems subject to a semilinear elliptic state equation together with the control constraints 0≤u≤1 and 驴u=m. Optimality conditions for this problem are derived and reformulated as a nonlinear, nonsmooth equation which is solved using a semismooth Newton method. A regularization of the nonsmooth equation is necessary to obtain the superlinear convergence of the semismooth Newton method. We prove that the solutions of the regularized problems converge to a solution of the original problem and a path-following technique is used to ensure a constant decrease rate of the residual. We show that, in certain situations, the optimal controls take 0---1 values, which amounts to solving a topology optimization problem with volume constraint.