=================== MultiIndices =================== Background ------------------- A multiindex is simply a length :math:D vector of nonnegative integers :math:\mathbf{p}=[p_1,p_2,\dots,p_D]. Multiindices are commonly employed for defining multivariate polynomial expansions and other function parameterizations. In these cases, sets of multiindices define the form of the expansion. **Example: Multivariate Polynomial** A multivariate polynomial :math:\Phi_{\mathbf{p}}(\mathbf{x}) : \mathbb{R}^D\rightarrow R can be defined as the product of :math:D univariate polynomials. Using monomials for example, .. math:: \Phi_{\mathbf{p}}(\mathbf{x}) = \prod_{i=1}^D x_i^{p_i} A multivariate polynomial expansion can then be written succinctly as .. math:: f(\mathbf{x}) = \sum_{\mathbf{p}\in\mathcal{S}} c_{\mathbf{p}} \Phi_{\mathbf{p}}(\mathbf{x}) where :math:\mathcal{S} is a set of multiindices and :math:c_{\mathbf{p}} are scalar coefficients. **Example: Wavelets** Multivariate polynomials are constructed from a tensor product of one-dimensional functions and each one-dimensional function depends on a single integer: the degree of the one-dimensional polynomial. This is a common way to define multivariate functions from indexed families of one-dimensional basis functions. In a general setting, however, the one-dimensional family does not need to be index by a single integer. Families of one-dimensional functions indexed with multiple integers can also be "tensorized" into multivariate functions. Wavelets are a prime example of this. A one dimensional wavelet basis contains functions of the form .. math:: \psi_{j,k}(x) = 2^{j/2}\psi(2^jx -k) where :math:j and :math:k are integers and :math:\psi :\mathbb{R}\rightarrow \mathbb{R} is an orthogonal wavelet. Unlike polynomials, two integers are required to index the one-dimensional family. Nevertheless, a multivariate wavelet basis can be defined through the tensor product of components in this family: .. math:: \Psi_{\mathbf{p}}(\mathbf{x}) = \prod_{i=1}^{D/2} \psi_{p_{2i},p_{2i+1}}(x_i) where :math:\Psi_{\mathbf{p}} : \mathbb{R}^{D/2}\rightarrow \mathbb{R} is a multivariate wavelet basis function in :math:D/2 dimensions and :math:\mathbf{p} is a multiindex with :math:D components. C++ Objects ------------------- .. toctree:: multiindices/multiindex multiindices/multiindexset multiindices/fixedmultiindexset multiindices/multiindexlimiter multiindices/multiindexneighborhood Definitions ------------------- .. topic:: Limiting Set In general, any length :math:D vector in :math:\mathbb{N}^D is a multiindex. In many adaptive approaches, however, it is useful to only consider multiindices in some subset :math:\mathcal{G}\subseteq \mathbb{N}^D. We call this subset the limiting set or, more loosely, the "limiter". In MParT, limiting sets are defined by functors that accept a MultiIndex and return true if the multiindex is in :math:\mathcal{G} and false otherwise. Some predefined limiting sets are defined in the MultiIndexLimiter namespace, but custom functors can also be employed. .. topic:: Neighbors In most parameterization applications, there is additional structure in the multiindex set that can be represented through edges on a graph. Each multiindex can be treated as a node in directed graph. Outgoing edges connect to other multiindices, which we call "forward neighbors", that represent higher order basis functions. Incoming edges come from multiindices, which we call "backward neighbors", that represent lower order basis functions. In the multivariate polynomial example above, the backward neighbors of a multiindex :math:\mathbf{j} are the multiindices that only differ from :math:\mbox{j} in one component, and in that component, the difference is -1. More precisely, the set of backward neighbors is .. math:: \mathcal{B}_{\mathbf{j}} = \{\mathbf{i} : \|\mathbf{i}-\mathbf{j}\|_1=1 \text{ and } \mathbf{i}_k \leq \mathbf{j}_k \text{ for all } k \}. The set of forward neighbors is similarly defined as .. math:: \mathcal{F}_{\mathbf{j}} = \{\mathbf{i} : \|\mathbf{i}-\mathbf{j}\|_1=1 \text{ and } \mathbf{i}_k \geq \mathbf{j}_k \text{ for all } k \} The neighborhood structure of a multindex set is defined through a child of the abstract MultiIndexNeighborhood class. .. topic:: Downward Closed A multiindex set :math:\mathcal{S} is downward closed if for any multiindex :math:\mathbf{j}\in\mathcal{S}, all backward neighbors of :math:\mathbf{j} are also in :math:\mathcal{S}. .. topic:: Margin For a multindex set :math:\mathcal{S}\subseteq\mathcal{G}, the margin of :math:\mathcal{S} is the set :math:\mathcal{M} \in \mathcal{G}\setminus\mathcal{S} containing indices that have at least one backward neighbor in :math:\mathcal{S}. .. topic:: Reduced Margin The reduced margin :math:\mathcal{M}_r\subseteq\mathcal{M} is a subset of the margin :math:\mathcal{M} containing indices that could be added to the set :math:\mathcal{S} while preserving the downward closed property of :math:\mathcal{S}. Multiindices in the reduced margin are often called admissible. .. topic:: Active/Inactive Indices The MultiIndexSet class stores both the set :math:\mathcal{S} and the margin :math:\mathcal{M}. In MParT, multiindices in :math:\mathcal{S} are sometimes called active, while multiindices in :math:\mathcal{M} are called inactive. Only active indices are included in the linear indexing. Inactive indices are hidden (i.e. not even considered) in all the public members of this class. Inactive multiindices are used behind the scenes to check admissability, to define the margin, and are added to the :math:\mathcal{S} during adaptation. .. topic:: Frontier The Frontier is similar but contains multiindices in :math:\mathcal{S} that have at least one forward neighbor in the margin :math:\mathcal{M}. We use the term "strict frontier" to describe the set of multiindices in :math:\mathcal{S} that have all of their forward neighbors in the margin.