Abstract
A combined method of penalties and stochastic quasigradients is used to solve convex variational inequalities by reducing them to various optimization problems in Hilbert space. Iterative algorithms are proposed, which produce the first approximation of the Fourier coefficients of the solution without assuming knowledge of the analytical characteristics of the problems. Theoretical and practical convergence is discussed.
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