System error
error:  Error in tempdir() using /tmp/jbovlaste_export/XXXXXXXXXX: Parent directory (/tmp/jbovlaste_export) does not exist at /srv/jbovlaste/current/lib/SimpleLaTeX.pm line 60. 

context: 


code stack: 
/usr/share/perl5/vendor_perl/Carp.pm:291 /usr/share/perl5/vendor_perl/File/Temp.pm:1704 /srv/jbovlaste/current/lib/SimpleLaTeX.pm:60 /srv/jbovlaste/current/lib/Wiki.pm:432 /srv/jbovlaste/current/dict/dhandler:317 /srv/jbovlaste/current/autohandler:4 
Error in tempdir() using /tmp/jbovlaste_export/XXXXXXXXXX: Parent directory (/tmp/jbovlaste_export) does not exist at /srv/jbovlaste/current/lib/SimpleLaTeX.pm line 60. Trace begun at /usr/share/perl5/vendor_perl/HTML/Mason/Exceptions.pm line 125 HTML::Mason::Exceptions::rethrow_exception('Error in tempdir() using /tmp/jbovlaste_export/XXXXXXXXXX: Parent directory (/tmp/jbovlaste_export) does not exist at /srv/jbovlaste/current/lib/SimpleLaTeX.pm line 60.^J') called at /usr/share/perl5/vendor_perl/Carp.pm line 291 Carp::croak('Error in tempdir() using /tmp/jbovlaste_export/XXXXXXXXXX: Parent directory (/tmp/jbovlaste_export) does not exist') called at /usr/share/perl5/vendor_perl/File/Temp.pm line 1704 File::Temp::tempdir('DIR', '/tmp/jbovlaste_export/', 'CLEANUP', 1) called at /srv/jbovlaste/current/lib/SimpleLaTeX.pm line 60 SimpleLaTeX::interpret('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}') called at /srv/jbovlaste/current/lib/Wiki.pm line 432 Wiki::mini('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}', 'en') called at /srv/jbovlaste/current/dict/dhandler line 317 HTML::Mason::Commands::__ANON__ at /usr/share/perl5/vendor_perl/HTML/Mason/Component.pm line 135 HTML::Mason::Component::run('HTML::Mason::Component::FileBased=HASH(0x7f4d38d7baa8)') called at /usr/share/perl5/vendor_perl/HTML/Mason/Request.pm line 1302 eval {...} at /usr/share/perl5/vendor_perl/HTML/Mason/Request.pm line 1292 HTML::Mason::Request::comp(undef, undef, undef) called at /usr/share/perl5/vendor_perl/HTML/Mason/Request.pm line 1355 HTML::Mason::Request::scomp('HTML::Mason::Request::ApacheHandler=HASH(0x7f4d20041438)', 'HTML::Mason::Component::FileBased=HASH(0x7f4d38d7baa8)') called at /srv/jbovlaste/current/autohandler line 4 HTML::Mason::Commands::__ANON__ at /usr/share/perl5/vendor_perl/HTML/Mason/Component.pm line 135 HTML::Mason::Component::run('HTML::Mason::Component::FileBased=HASH(0x7f4d38d96e58)') called at /usr/share/perl5/vendor_perl/HTML/Mason/Request.pm line 1300 eval {...} at /usr/share/perl5/vendor_perl/HTML/Mason/Request.pm line 1292 HTML::Mason::Request::comp(undef, undef, undef) called at /usr/share/perl5/vendor_perl/HTML/Mason/Request.pm line 481 eval {...} at /usr/share/perl5/vendor_perl/HTML/Mason/Request.pm line 481 eval {...} at /usr/share/perl5/vendor_perl/HTML/Mason/Request.pm line 433 HTML::Mason::Request::exec('HTML::Mason::Request::ApacheHandler=HASH(0x7f4d20041438)') called at /usr/share/perl5/vendor_perl/HTML/Mason/ApacheHandler.pm line 168 HTML::Mason::Request::ApacheHandler::exec('HTML::Mason::Request::ApacheHandler=HASH(0x7f4d20041438)') called at /usr/share/perl5/vendor_perl/HTML/Mason/ApacheHandler.pm line 825 HTML::Mason::ApacheHandler::handle_request('HTML::Mason::ApacheHandler=HASH(0x7f4d38113d40)', 'Apache2::RequestRec=SCALAR(0x7f4d0808a368)') called at (eval 28) line 8 HTML::Mason::ApacheHandler::handler('HTML::Mason::ApacheHandler', 'Apache2::RequestRec=SCALAR(0x7f4d0808a368)') called at e line 0 eval {...} at e line 0