On nondegenerate M-stationary points for sparsity constrained nonlinear optimization
From MaRDI portal
Publication:2114575
DOI10.1007/s10898-021-01070-7zbMath1486.90191arXiv1912.04087OpenAlexW3193729844MaRDI QIDQ2114575
Publication date: 15 March 2022
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1912.04087
Related Items (3)
Critical point theory for sparse recovery ⋮ Inexact penalty decomposition methods for optimization problems with geometric constraints ⋮ Optimality conditions for mathematical programs with orthogonality type constraints
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On solutions of sparsity constrained optimization
- The first-order necessary conditions for sparsity constrained optimization
- Constraint qualifications and optimality conditions for optimization problems with cardinality constraints
- Mathematical programs with vanishing constraints: critical point theory
- Topological aspects of nonsmooth optimization.
- Nonlinear optimization in finite dimensions. Morse theory, Chebyshev approximation, transversality, flows, parametric aspects
- Second-order optimality conditions and improved convergence results for regularization methods for cardinality-constrained optimization problems
- Optimality conditions for sparse nonlinear programming
- On the Minimization Over Sparse Symmetric Sets: Projections, Optimality Conditions, and Algorithms
- Sparsity Constrained Nonlinear Optimization: Optimality Conditions and Algorithms
- Morse Theory. (AM-51)
- Differential Topology
- Variational Analysis
- Proximal Mapping for Symmetric Penalty and Sparsity
- MPCC: Critical Point Theory
- Sparse Approximation via Penalty Decomposition Methods
- Mathematical Programs with Cardinality Constraints: Reformulation by Complementarity-Type Conditions and a Regularization Method
- Compressed sensing
- Simplicial homotopy theory
This page was built for publication: On nondegenerate M-stationary points for sparsity constrained nonlinear optimization