specifics X78Structured and Unstructured Sparsity - Lots of 'free' wins from exploring sparsity in modern ML models - Can often prune models to 80%+ sparsity(with retraining) - Massive speedups combined

sparse matrices differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern as a given sparse matrix S, or that the resulting sparse matrix has density d, i.e. each dimensions m x n with structural zeros at S[I[k], J[k]]. This method can be used to construct the sparsity pattern of the matrix, and is more efficient than using e.g. sparse(I, J, zeros(length(I))). For C::Sparse, update::Cint) Update an LDLt or LLt Factorization F of A to a factorization of A ± C*C'. If sparsity preserving factorization is used, i.e. L*L' == P*A*P' then the new factor will be L*L' == P*A*P'

sparse matrices differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern as a given sparse matrix S, or that the resulting sparse matrix has density d, i.e. each dimensions m x n with structural zeros at S[I[k], J[k]]. This method can be used to construct the sparsity pattern of the matrix, and is more efficient than using e.g. sparse(I, J, zeros(length(I))). For C::Sparse, update::Cint) Update an LDLt or LLt Factorization F of A to a factorization of A ± C*C'. If sparsity preserving factorization is used, i.e. L*L' == P*A*P' then the new factor will be L*L' == P*A*P'

sparse matrices differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern as a given sparse matrix S, or that the resulting sparse matrix has density d, i.e. each dimensions m x n with structural zeros at S[I[k], J[k]]. This method can be used to construct the sparsity pattern of the matrix, and is more efficient than using e.g. sparse(I, J, zeros(length(I))). For C::Sparse, update::Cint) Update an LDLt or LLt Factorization F of A to a factorization of A ± C*C'. If sparsity preserving factorization is used, i.e. L*L' == P*A*P' then the new factor will be L*L' == P*A*P'

sparse matrices differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern as a given sparse matrix S, or that the resulting sparse matrix has density d, i.e. each dimensions m x n with structural zeros at S[I[k], J[k]]. This method can be used to construct the sparsity pattern of the matrix, and is more efficient than using e.g. sparse(I, J, zeros(length(I))). For C::Sparse, update::Cint) Update an LDLt or LLt Factorization F of A to a factorization of A ± C*C'. If sparsity preserving factorization is used, i.e. L*L' == P*A*P' then the new factor will be L*L' == P*A*P'

sparse matrices differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern as a given sparse matrix S, or that the resulting sparse matrix has density d, i.e. each dimensions m x n with structural zeros at S[I[k], J[k]]. This method can be used to construct the sparsity pattern of the matrix, and is more efficient than using e.g. sparse(I, J, zeros(length(I))). For C::Sparse, update::Cint) Update an LDLt or LLt Factorization F of A to a factorization of A ± C*C'. If sparsity preserving factorization is used, i.e. L*L' == P*A*P' then the new factor will be L*L' == P*A*P'

sparse matrices differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern as a given sparse matrix S, or that the resulting sparse matrix has density d, i.e. each dimensions m x n with structural zeros at S[I[k], J[k]]. This method can be used to construct the sparsity pattern of the matrix, and is more efficient than using e.g. sparse(I, J, zeros(length(I))). For

sparse matrices differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern as a given sparse matrix S, or that the resulting sparse matrix has density d, i.e. each dimensions m x n with structural zeros at S[I[k], J[k]]. This method can be used to construct the sparsity pattern of the matrix, and is more efficient than using e.g. sparse(I, J, zeros(length(I))). For

sparse matrices differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern as a given sparse matrix S, or that the resulting sparse matrix has density d, i.e. each dimensions m x n with structural zeros at S[I[k], J[k]]. This method can be used to construct the sparsity pattern of the matrix, and is more efficient than using e.g. sparse(I, J, zeros(length(I))). For

sparse matrices differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern as a given sparse matrix S, or that the resulting sparse matrix has density d, i.e. each dimensions m x n with structural zeros at S[I[k], J[k]]. This method can be used to construct the sparsity pattern of the matrix, and is more efficient than using e.g. sparse(I, J, zeros(length(I))). For