This work focuses on convergence analysis of the projected gradient method for solving constrained convex minimization problem in Hilbert spaces. We show that the sequence of points generated by the method employing the Armijo linesearch converges weakly to a solution of the considered convex optimization problem. Weak convergence is established by assuming convexity and Gateaux differentiability of the objective function, whose Gateaux derivative is supposed to be uniformly continuous on bounded sets. Furthermore, we propose some modifications in the classical projected gradient method in order to obtain strong convergence. The new variant has the following desirable properties: the sequence of generated points is entirely contained in a ball with diameter equal to the distance between the initial point and the solution set, and the whole sequence converges strongly to the solution of the problem that lies closest to the initial iterate. Convergence analysis of both methods is presented without Lipschitz continuity assumption.

We describe a relationship between work of Laksov, Gatto, and their collaborators on realizations of (generalized) Schubert calculus of Grassmannians, and the geometric Satake correspondence of Lusztig, Ginzburg, and Mirkovi\'c and Vilonen. Along the way we obtain new proofs of equivariant Giambelli formulas for the ordinary and orthogonal Grassmannians, as well as a simple derivation of the "rim-hook" rule for computing in the equivariant quantum cohomology of the Grassmannian.

In this paper, two types of linear estimators are considered for three related estimation problems involving set-theoretic uncertainty pertaining to $\mathcal{H}_{2}$ and $\mathcal{H}_{\infty}$ balls of frequency-responses. The problems at stake correspond to robust $\mathcal{H}_{2}$ and $\mathcal{H}_{\infty}$ in the face of non-parametric "channel-model" uncertainty and to a nominal $\mathcal{H}_{\infty}$ estimation problem. The estimators considered here are defined by minimizing the worst-case squared estimation error over the "uncertainty set" and by minimizing an average cost under the constraint that the worst-case error of any admissible estimator does not exceed a prescribed value. The main point is to explore the derivation of estimators which may be viewed as less conservative alternatives to minimax estimators, or in other words, that allow for trade-offs between worst-case performance and better performance over "large" subsets of the uncertainty set. The "average costs" over $\mathcal{H}_{2}-$signal balls are obtained as limits of averages over sets of finite impulse responses, as their length grows unbounded. The estimator design problems for the two types of estimators and the three problems addressed here are recast as semi-definite programming problems (SDPs, for short). These SDPs are solved in the case of simple examples to illustrate the potential of the "average cost/worst-case constraint" estimators to mitigate the inherent conservatism of the minimax estimators.

Discrete exterior calculus offers a coordinate--free discretization of exterior calculus especially suited for computations on curved spaces. In this work, we present a wedge product on 2--dimensional pseudomanifolds, whose faces are any polygons. We prove that this polygonal wedge product is compatible with the discrete exterior derivative in the sense that it satisfies the Leibniz product rule. We thus extend previously studied discretizations of wedge products from simplicial or quadrilateral meshes to surface meshes whose faces are arbitrary simple polygons. We also prove that our discrete wedge product corresponds to a cup product of cochains on 2--pseudomanifolds. By rigorously justifying our construction we add another piece to ever evolving discrete versions of exterior calculus.