In this paper, we establish a strong convergence theorem regarding a regularized variant of the projected subgradient method for nonsmooth, nonstrictly convex minimization in real Hilbert spaces. Only one projection step is needed per iteration and the involved stepsizes are controlled so that the algorithm is of practical interest. To this aim, we develop new techniques of analysis which can be adapted to many other non-Fejerian methods.

We consider a semilinear eigenvalue problem with a nonsmooth potential (hemivariational inequality). Using a nonsmooth analog of the local Ambrosetti–Rabinowitz condition (AR-condition), we show that the problem has a nontrivial smooth solution. In the scalar case, we show that we can relax the local AR-condition. Finally, for the resonant λ = λ1 problem, using the nonsmooth version of the local linking theorem, we show that the problem has at least two nontrivial solutions. Our approach is variational, using minimax methods from the nonsmooth critical point theory.

The purpose of this paper is to investigate topological properties and stability of solution sets in parametric variational relation problems. The results of the paper give a unifying way to treat these questions in the theory of variational inequalities, variational inclusions and equilibrium problems.

In this paper we study the asymptotic behaviour of solutions of the phase-field system on an unbounded domain. We do not assume conditions on the non-linear term ensuring the uniqueness of the Cauchy problem, so that we have to work with multivalued semiflows rather than with semigroups of operators. In this way we prove the existence of a global attractor by considering the convergence in an appropriate weighted space. This result is also new for more restrictive conditions, which guarantee the uniqueness of solutions.

In the present paper we prove that, under some suitable conditions on multifunctions \(C\!: {I\!\!R}^l\rightrightarrows {I\!\!R}^n\), \(F\!:{I\!\!R}^d\times {I\!\!R}^n\rightrightarrows {I\!\!R}^m\), and \(K\!:{I\!\!R}^l\times {I\!\!R}^n\rightrightarrows {I\!\!R}^m\), the generalized perturbation multifunction \(G\!: {I\!\!R}^d\times{I\!\!R}^l\times {I\!\!R}^m\rightrightarrows {I\!\!R}^n\), of the form $$G(\mu,\lambda,\nu)=\{x\in C(\lambda)\ |\ \nu\in F(\mu,x)+K(\lambda,x)\},$$ is proto-differentiable at (μ, λ, ν) relative to x ∈ G(μ, λ, ν). Moreover, in a special case, where K(λ, x) is a normal cone to C(λ) at x, we also provide sufficient conditions for G(·) to be single-valued on a neighborhood of (μ, λ, ν) and semi-differentiable at (μ, λ, ν).

In this paper, we relate the nonconvex separation principle (SP) established in Zheng and Ng (Math Program Series A, 104:69–90, 2005) with the known extremal principle (EP), Ekeland variational principle (EVP) and the completeness of the underlying space. In particular, we show that (SP) is equivalent to the completeness in a normed linear space setting. Moreover, as an application, an extension of the strict convex separation theorem is presented for Banach space.

This paper is concerned with the study of solution stability of a parametric generalized variational inequality in reflexive Banach spaces. Under the requirements that the operator of a unperturbed problem is of class (S) + and operators under consideration are pseudo-monotone and demicontinuous, we show that the solution map of a parametric generalized variational inequality is lower semicontinuous. The obtained results are proved without conditions related to the degree theory and the metric projection.

It is well known that mathematical programs with equilibrium constraints (MPEC) violate the standard constraint qualifications such as Mangasarian–Fromovitz constraint qualification (MFCQ) and hence the usual Karush–Kuhn–Tucker conditions cannot be used as stationary conditions unless relatively strong assumptions are satisfied. This observation has led to a number of weaker stationary conditions, with Mordukhovich stationary (M-stationary) condition being the strongest among the weaker conditions. In nonlinear programming, it is known that MFCQ leads to an exact penalization. In this paper we show that MPEC GMFCQ, an MPEC variant of MFCQ, leads to a partial exact penalty where all the constraints except a simple linear complementarity constraint are moved to the objective function. The partial exact penalty function, however, is nonsmooth. By smoothing the partial exact penalty function, we design an algorithm which is shown to be globally convergent to an M-stationary point under an extended version of the MPEC GMFCQ.

In this paper we provide an extension of the Viability and Invariance Theorems in the Wasserstein metric space of probability measures with finite quadratic moments in ℝ d for controlled systems of which the dynamic is bounded and Lipschitz. Then we characterize the viability and invariance kernels as the largest viability (resp. invariance) domains. As application of our result we consider an optimal control problem of Mayer type with lower semicontinuous cost function for the same controlled system with uncertainty on the initial state modeled by a probability measure. Following Frankowska, we prove using the epigraphical viability approach that the value function is the unique lower semicontinuous proximal episolution of a suitable Hamilton Jacobi equation.

Motivated by a classical result concerning the e-subdifferential of the sum of two proper, convex and lower semicontinuous functions, we give in this paper a similar result for the enlargement of the sum of two maximal monotone operators defined on a Banach space. This is done by establishing a necessary and sufficient condition for a bivariate inf-convolution formula.