Mathematics of Operations Research · 2002 · 228 citations · 10 references
Natural SciencesComputer ScienceComputational GeometryDerivativesOptimizationConvex AnalysisConvex OptimizationMatrix ComputationNonlinear ProgrammingMatrix NormsNewton ConvergenceNonsmooth OptimizationSemidefinite ProgrammingSmooth Optimization
Matrix-valued functions play an important role in the development of algorithms for semidefinite programming problems. This paper studies generalized differential properties of such functions related to nonsmooth-smoothing Newton methods. The first part of this paper discusses basic properties such as the generalized derivative, Rademacher's theorem, ℬ-derivative, directional derivative, and semismoothness. The second part shows that the matrix absolute-value function, the matrix semidefinite-projection function, and the matrix projective residual function are strongly semismooth.
10
A nonsmooth version of Newton's method
Liqun Qi, Jie Sun · Mathematical Programming · 1993 · 1.5K citations
Nonsmooth Equations: Motivation and Algorithms
Jong‐Shi Pang, Liqun Qi · SIAM Journal on Optimization · 1993 · 357 citations