The phrase "a much larger matrix" is correct and usable in written English.
You can use it when discussing a matrix that is significantly bigger in size or complexity compared to another matrix.
Example: "In our analysis, we found that a much larger matrix was required to accurately represent the data we were working with."
Alternatives: "a significantly larger matrix" or "a considerably larger matrix".
We present a simple algorithm for detecting low-rank submatrices from within a much larger matrix.
Instead, although we still are doing a Metropolis step, it will correspond to a much larger matrix which is defined only implicitly. 1.
Iterate the same matrix again with more colors (iterations) to reveal the next few layers, or better, draw up a much larger matrix for a longer-term project!
This happens because the number of items in MUSIC (3121) is much higher than the number of users (785), leading to much larger matrices when using item-based algorithms.
Although such models provided relatively accurate predictions for short binding motifs [ 14], alternative models have been developed to encode short-range information through building much larger matrices for subsequences (k-mers) [ 5, 15].
"While we're gratified by the demise of T.I.A. and the continuing collapse of Matrix, there's a much larger phenomenon of widespread surveillance that still occurs out there," he said.
But we can find signature matrices that can support a much larger number of users while the transmission is still errorless in the absence of noise.
Consequently, the STRIKE matrix could be estimated on a much larger reference dataset.
The problem in the construction of an optimal design matrix as a particular subset of a much larger universal set of potential solutions needs specially problem-adapted genetic operators.
The present application involves only small matrices; input-output applications are generally on a much larger scale but the basic ideas are the same.
Since the STRIKE matrix does not require pairs of homologous structures, far fewer parameters can be estimated on a much larger dataset compared with CAO, thus avoiding both underdetermination and overfitting problems.
