daigno
fu'ivla x_{1} (ordered list) is a sampling of entries of matrix/tensor x_{2} in which exactly one entry is sampled from each row and/or column (etc.) between entries x_{3} (list; default: the largest 'square'/'hypercubic' sampling possible in the entire tensor starting with the first entry, see notes) inclusively following selection procedure/rule/function/order x_{4} (default: diagonally, see notes), where the tensor/matrix is expressed in basis/under conditions x_{5} Entries of the list in x3 need not actually be sampled; the entries listed are merely to name the minimal and maximal indices between which the sampling may be drawn. Thus, the indices/labels specified are included in the range of sampling; id est: if the matrix entries listed belong to the ith row and jth column and the (i+n)th row and (j+m)th column respectively (for positive integers i,j,n,m), then the sampling will be conducted in all rows of number between (and including) i and i+n (yielding n+1 sampled rows) and in all columns of number between (and including) j and j+m (yielding m+1 sampled columns). The default diagonal sampling procedure for x4 is as follows: The first sampled entry has the minimum allowed (as specified in x3) indices. All latter sampled entries (by default) have indices of the immediately previous sampled entry each augmented by 1. (Which is to say that if the kth sampled entry has indices (x,y,...), in that order, then the (k+1)th sampled entry has indices (x+1,y+1,...), in that order and where each subsequent index would be the respective index of the kth sampled entry augmented by 1). The process terminates generally whenever exactly one entry is sampled from each of the rows, each of the columns, etc. of the tensor. In the default, the process terminates when at least one of the indices of a sampled entry of the tensor is as large as possible in the range specified by x3. Thus, in order to reconcile the general and the default termination conditions, the range specified by x3 must be compatible with both; id est: it must be a rdimensional hypercube of entries, so to speak, where r is the rank of tensor x2. The default for sampling range x3 is between and including the entry in the first row and first column (etc.) and the entry in the last row and last column (etc.) for an rdimensional hypercube tensor (meaning that each row, column, etc. of the tensor has exactly the same number of entries as the others). Generally, the default range begins with the entry of indices each minimal in the tensor (called 'the first entry') and extends to include ("draw") the maximal rdimensional hypercube of entries in the tensor with one vertex on the first entry; in other words, if the minimum of the set of maximal indices in the tensor is g, then the sampling range is every row between the first and the gth, every column between the first and the gth, etc. Generally, the sampling range must be an rdimensional orthotope of some positive size (that is to say: including at least one entry) no larger than the tensor itself, but with the freedom to place at most r of its vertices among the entries thereof; if the default sampling procedure x4 is being used, then the rdimensional orthotope must be an rdimensional hypercube. Generalizes to any tensor, but is only interesting for tensors of rank at least 1. Any mention of geometric terminology (such as mention of diagonals, orthotopes, etc.) in the definition or notes of this word should be interpreted cautiously and is not necessarily good Lojbanic practice; such terminology should not necessarily be emulated in practicing Lojbanic thought or speech. Not for use for geometric diagonals (such as between vertices); confer: digno.


digno
experimental gismu x_{1} is aligned diagonally along/between nonadjacent vertices x_{2} as in polytope x_{3}; x_{1} is a diagonal line segment/linear manifold of lower dimension as viewed in frame of reference x_{3}; x_{1} is crooked (one sense), skew (one sense, see notes), offkilter (one sense), away from center/offcenter, nonorthogonal/not perpendicular nor parallel, at an angle, perhaps nonvertical and nonhorizontal, diagonal to x_{2} in figure/coordinate system x_{3}. Not for use in: entries of tensors/matrices (confer: daigno), certain geometric meanings (such as with Cartesian products), etc. Only for purely 'visual' geometric objects/figures/frames. The polytope in question need not actually be 'drawn'; an oriented frame of reference naturally 'projects' a polytopic sense onto all objects. x1 can be any linear manifold of lower dimension than the space in which it is embedded (defined by x3). The skewness is not relative to another linear manifold in some higherdimensional space (the usual definition of "skew" in geometry)  it is simply a skewness (in a layperson sense) relative to points in a figure or axis in a coordinate system. Proposed by Gleki.


aigne
fu'ivla x_{1} is an eigenvalue (or zero) of linear transformation/square matrix x_{2}, associated with/'owning' all vectors in generalized eigenspace x_{3} (implies neither nondegeneracy nor degeneracy; default includes the zero vector) with 'eigenspacegeneralization' power/exponent x_{4} (typically and probably by cultural default will be 1), with algebraic multiplicity (of eigenvalue) x_{5} For any eigenvector v in generalized eigenspace x_{3} of linear transformation x_{2} for eigenvalue x_{1}, where I is the identity matrix/transformation that works/makes sense in the context, the following equation is satisfied: ((x_{2}  x_{1} I)^x_{4})v = 0. When the argument of x_{4} is 1, the generalized eigenspace x_{3} is simply a strict/simple/basic eigenspace; this is the typical (and probable cultural default) meaning of this word. x_{4} will typically be restricted to integer values k > 0. x_{2} should always be specified (at least implicitly by context), for an eigenvalue does not mean much without the linear transformation being known. However, since one usually knows the said linear transformation, and since the basic underlying relationship of this word is "eigenness", the eigenvalue is given the primary terbri (x_{1}). When filling x_{3} and/or x_{4}, x_{2} and x_{1} (in that order of importance) should already be (at least contextually implicitly) specified. x_{3} is the set of all eigenvectors of linear transformation x_{2}, endowed with all of the typical operations of the vector space at hand. The default includes the zero vector (else the x_{3} eigenspace is not actually a vector space); normally in the context of mathematics, the zero vector is not considered to be an eigenvector, but by this definition it is included. Thus, a Lojban mathematician would consider the zero vector to be an (automatic) eigenvector of the given (in fact, any) linear transformation (particularly ones represented by a square matrix in a given basis). This is the logically most basic definition, but is contrary to typical mathematical culture. This word implies neither nondegeneracy nor degeneracy of eigenspace x_{3}. In other words there may or may not be more than one linearly independent vector in the eigenspace x_{3} for a given eigenvalue x_{1} of linear transformation x_{2}. x_{3} is the unique generalized eigenspace of x_{2} for given values of x_{1} and x_{4}. x_{1} is not necessarily the unique eigenvalue of linear transformation x_{2}, nor is its multiplicity necessarily 1 for the same. Beware when converting the terbri structure of this word. In fact, the set of all eigenvalues for a given linear transformation x_{2} will include scalar zero (0); therefore, any linear transformation with a nontrivial set of eigenvalues will have at least two eigenvalues that may fill in terbri x_{1} of this word. The 'eigenvalue' of zero for a proper/nice linear transformation will produce an 'eigenspace' that is equivalent to the entire vector space at hand. If x_{3} is specified by a set of vectors, the span of that set should fully yield the entire eigenspace of the linear transformation x_{2} associated with eigenvalue x_{1}, however the set may be redundant (linearly dependent); the zero vector is automatically included in any vector space. A multidimensional eigenspace (that is to say a vector space of eigenvectors with dimension strictly greater than 1) for fixed eigenvalue and linear transformation (and generalization exponent) is degenerate by definition. The algebraic multiplicity x_{5} of the eigenvalue does not entail degeneracy (of eigenspace) if greater than 1; it is the integer number of occurrences of a given eigenvalue x_{1} in the multiset of eigenvalues (spectrum) of the given linear transformation/square matrix x_{2}. In other words, the characteristic polynomial can be factored into linear polynomial primes (with root x_{1}) which are exponentiated to the power x_{5} (the multiplicity; notably, not x_{4}). For x_{4} > x_{5}, the eigenspace is trivial. x_{2} may not be diagonalizable. The scalar zero (0) is a naturally permissible argument of x_{1} (unlike some cultural mathematical definitions in English). Eigenspaces retain the operations and properties endowing the vectorspaces to which they belong (as subspaces). Thus, an eigenspace is more than a set of objects: it is a set of vectors such that that set is endowed with vectorspace operators and properties. Thus klesi alone is insufficient. But the set underlying eigenspace x_{3} is a type of klesi, with the property of being closed under linear transformation x_{2} (up to scalar multiplication). The vector space and basis being used are not specified by this word. Use this word as a seltau in constructions such as "eigenket", "eigenstate", etc. (In such cases, te aigne is recommended for the typical English usages of such terms. Use zei in lujvo formed by these constructs. The term "eigenvector" may be rendered as cmima be le te aigne). See also gei'ai, klesi, daigno
