Abstract
S. M. Robinson published in 1980 a powerful theorem about solutions to certain “generalized equations” corresponding to parameterized variational inequalities which could represent the first-order optimality conditions in nonlinear programming, in particular. In fact, his result covered much of the classical implicit function theorem, if not quite all, but went far beyond that in ideas and format. Here, Robinson’s theorem is viewed from the perspective of more recent developments in variational analysis as well as some lesser-known results in the implicit function literature on equations, prior to the advent of generalized equations. Extensions are presented which fully cover such results, translating them at the same time to generalized equations broader than variational inequalities. Robinson’s notion of first-order approximations in the absence of differentiability is utilized in part, but even looser forms of approximation are shown to furnish significant information about solutions.
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Dedicated to Stephen M. Robinson with deep respect for his fundamental contributions to optimization theory and beyond.
This work was supported by National Science Foundation grant DMS 0104055.
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Dontchev, A.L., Rockafellar, R.T. Robinson’s implicit function theorem and its extensions. Math. Program. 117, 129–147 (2009). https://doi.org/10.1007/s10107-007-0161-1
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DOI: https://doi.org/10.1007/s10107-007-0161-1
Keywords
- Inverse and implicit function theorems
- Calmness
- Lipschitz modulus
- First-order approximations
- Semiderivatives
- Variational inequalities