AI & ML
An Alternating Direction Method of Multipliers for Topology Optimization
arXiv cs.NA0 views
arXiv:2509.19888v2 Announce Type: replace-cross Abstract: We consider a class of integer-constrained optimization problems governed by partial differential equation (PDE) constraints and regularized via total variation (TV) in the context of topology optimization. The presence of discrete design variables, nonsmooth regularization, and non-convex objective renders the problem computationally challenging. To address this, we adopt the alternating direction method of multipliers (ADMM) framework, which enables a decomposition of the original problem into simpler subproblems that can be solved efficiently. The augmented Lagrangian formulation ensures consistency across variable updates while facilitating convergence under appropriate conditions.
Source:https://arxiv.org/abs/2509.19888PreviousMicro-macro and macro-macro limits for controlled leader-follower systemsNextRecursive Sketched Interpolation: Efficient Hadamard Products of Tensor Trains
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