The least slope of a convex function and the maximal monotonicity of its subdifferential
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Publication:1321100
DOI10.1007/BF00940043zbMath0795.49016OpenAlexW2107005342MaRDI QIDQ1321100
Publication date: 27 September 1994
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/bf00940043
subdifferentialsteepest descentseparation theoremsandwich theoremmaximal monotoneconvex lower- semicontinuous functionleast slope of a convex function
Related Items
Swimming below icebergs ⋮ Error bounds revisited ⋮ Subtangents with controlled slope ⋮ Subdifferential characterization of approximate convexity: The lower semicontinuous case ⋮ The maximal monotonicity of the subdifferentials of convex functions: Simons' problem ⋮ Mean Value Property and Subdifferential Criteria for Lower Semicontinuous Functions ⋮ Subdifferential estimate of the directional derivative, optimality criterion and separation principles ⋮ Bounded approximants to monotone operators on Banach spaces ⋮ Error bounds and metric subregularity ⋮ Subdifferentials are locally maximal monotone ⋮ Remarks on subgradients and \(\varepsilon\)-subgradients
Cites Work
- A note on epsilon-subgradients and maximal monotonicity
- Convex functions, monotone operators and differentiability
- On the maximal monotonicity of subdifferential mappings
- Subgradients of a convex function obtained from a directional derivative
- Optimization and nonsmooth analysis
- On the Identification of Active Constraints
- On the Subdifferentiability of Convex Functions
