Equivalent differentiable optimization problems and descent methods for asymmetric variational inequality problems. (English) Zbl 0756.90081

The variational inequality problem is (1): Find \(x^*\in S\subset\mathbb{R}^ n\) such that \(\langle F(x^*),x-x^*\rangle\geq 0\), \(\forall x\in\mathbb{R}^ n\) where \(S\neq\emptyset\) is closed and convex and \(F: \mathbb{R}^ n\to\mathbb{R}^ n\). Now with \(G\) being any \(n\times n\) positive definite matrix consider the program (2): \(\min\{\phi(y): y\in S\}\) where \(\phi(y)=\langle F(x),(y-x)\rangle+{1\over 2}\langle(y-x),G(y-x)\rangle\), and let \(-f(x): \mathbb{R}^ n\to\mathbb{R}\) be the optimal objective value of \(\phi(y)\) in (2).
The main result is now: (i) \(f(x)\geq 0\), \(\forall x\in S\), and (ii) \(x^*\) solves (1) if and only if it solves the program (3): \(\min\{f(x): x\in S\}\) and that happens if and only if \(f(x^*)=0\), \(x^*\in S\). Moreover \(f\) is continuously differentiable (continuous) if \(F\) is continuously differentiable (continuous). In the first case descent methods are presented to solve the program (3) by an iteration process.
A list of sixteen references closes the paper.


90C30 Nonlinear programming
49J40 Variational inequalities
90-08 Computational methods for problems pertaining to operations research and mathematical programming
90C33 Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming)
90C99 Mathematical programming
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