I shall discuss the theory of multi-norms. This has connections with norms on tensor products and with absolutely summing operators. There are many examples, some of which will be mentioned. In particular we shall describe multi-norms based on Banach lattices, define multi-bounded operators, and explain their connections with regular operators on lattices. We have new results on the equivalences of multi-norms. The theory of decompositions of Banach lattices with respect to the canonical 'Banach-lattice multi-norm' has a pleasing form because of a substantial theorem of Nigel Kalton that I shall state and discuss. I shall also discuss brie y a generalization that gives 'pmulti-norms' (for $1\leq p\leq1$) and an extension of a representation theorem of Pisier that shows that many pmulti-norms are 'sous-espaces de treillis'. The theory is based on joint work with Maxim Polyakov (deceasead), Hung Le Pham (Wellington), Matt Daws (Leeds), Paul Ramsden (Leeds), Oscar Blasco (Valencia), Niels Laustsen (Lancaster), Timur Oikhberg (Illinois), and Vladimir Troitsky (Edmonton).

multi-norms - equivalences - absolutely summing operators - tensor products[-]

Let $X$ be a Banach space of holomorphic functions on the unit disk. A linear polynomial approximation scheme for $X$ is a sequence of bounded linear operators $T_{n} :X\rightarrow X$ with the property that, for each $f\in X$, the functions $T_{n}\left ( f \right )$ are polynomials converging to $f$ in the norm of the space. We completely characterize those spaces $X$ that admit a linear polynomial approximation scheme. In particular, we show that it is not sufficient merely that polynomials be dense in $X$. (Joint work with Javad Mashreghi).[-]

Tensor methods have emerged as an indispensable tool for the numerical solution of high-dimensional problems in computational science, and in particular problems arising in stochastic and parametric analyses. In many practical situations, the approximation of functions of multiple parameters (or random variables) is made computationally tractable by using low-rank tensor formats. Here, we present some results on rank-structured approximations and we discuss the connection between best approximation problems in tree-based low-rank formats and the problem of finding optimal low-dimensional subspaces for the projection of a tensor. Then, we present constructive algorithms that adopt a subspace point of view for the computation of sub-optimal low-rank approximations with respect to a given norm. These algorithms are based on the construction of sequences of suboptimal but nested subspaces.

Keywords: high dimensional problems - tensor numerical methods - projection-based model order reduction - low-rank tensor formats - greedy algorithms - proper generalized decomposition - uncertainty quantification - parametric equations[-]