Expand and update examples, improve spmm performance

[rileyjmurray]

Changes to examples

• The total least squares examples now run the classical algorithm in addition to the randomized algorithm. We compare runtime and solution quality for the two methods.
• I've added three examples for low-rank approximation of sparse matrices.
  • QB-based SVD of randomly generated synthetic test matrices (rank one plus noise).
  • QB-based SVD of any sparse matrix in Matrix Market format.
  • Low-rank QRCP of a sparse matrix in Matrix Market format.

Changes to sparse matrix functionality

• I've added two kernel implementations for SPMM of row-major data. This was needed to address cache inefficiencies revealed in the existing SPMM kernels during low-rank QRCP benchmarking.
• I've removed the requirement for A.reserve((int64_t) 10) being able to execute for a matrix A that's compatible with the SpMatrix concept. Apparently this didn't work with sparse matrices marked as const.

I've also added two files of developer notes: RandBLAS/DevNotes.md and RandBLAS/sparse_data/DevNotes.md.

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