Reformulates SDCMPCC via spectral decomposition of complementarity structure and proves augmented Lagrangian accumulation points are W-stationary (or C-stationary under stricter subproblem conditions).
Shape Optimization inW1,∞ with Geometric Constraints: a Study in Distributed-Memory Systems
6 Pith papers cite this work. Polarity classification is still indexing.
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A proximal limited-memory quasi-Newton scheme is developed for nonsmooth nonconvex optimization, with global convergence proven under mild assumptions and rates under the Kurdyka-Lojasiewicz property.
Piecewise M-stationarity is equivalent to B-stationarity for MPCCs under MPCC-ACQ and reduces the cost of verifying stationarity for NCP-based algorithms.
Approximate one-time preconditioning in face for MPGP algorithms yields error bounds, a sharp condition-number estimate, and large observed speedups on quadratic programs with constraints.
A hybrid numerical scheme for shape optimization first applies phase field methods to obtain an initial domain and then switches to a sharp interface solver on a mesh constructed from the phase field's post-processed zero level set.
A solver-agnostic condensing reformulation for linear-quadratic optimization with polyhedral and geometric constraints that preserves augmented-Lagrangian convergence while improving computational speed.
citing papers explorer
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Augmented Lagrangian methods for nonlinear semidefinite programming with complementarity constraints
Reformulates SDCMPCC via spectral decomposition of complementarity structure and proves augmented Lagrangian accumulation points are W-stationary (or C-stationary under stricter subproblem conditions).
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Proximal Limited-Memory Quasi-Newton Methods for Nonsmooth Nonconvex Optimization
A proximal limited-memory quasi-Newton scheme is developed for nonsmooth nonconvex optimization, with global convergence proven under mild assumptions and rates under the Kurdyka-Lojasiewicz property.
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Piecewise M-Stationarity and Related Algorithms for Mathematical Programs with Complementarity Constraints
Piecewise M-stationarity is equivalent to B-stationarity for MPCCs under MPCC-ACQ and reduces the cost of verifying stationarity for NCP-based algorithms.
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Accelerating MPGP-type Methods Through Preconditioning
Approximate one-time preconditioning in face for MPGP algorithms yields error bounds, a sharp condition-number estimate, and large observed speedups on quadratic programs with constraints.
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Combining diffuse and sharp interface methods in shape optimisation
A hybrid numerical scheme for shape optimization first applies phase field methods to obtain an initial domain and then switches to a sharp interface solver on a mesh constructed from the phase field's post-processed zero level set.
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A condensing approach for linear-quadratic optimization with geometric constraints
A solver-agnostic condensing reformulation for linear-quadratic optimization with polyhedral and geometric constraints that preserves augmented-Lagrangian convergence while improving computational speed.