The phrase "a subset of squares" is correct and usable in written English.
It can be used in mathematical or logical contexts to describe a smaller group that is contained within a larger group of squares.
Example: "In geometry, we often study a subset of squares that meet specific criteria, such as having equal side lengths."
Alternatives: "a portion of squares" or "a segment of squares".
A subset of the square is "the whale," a square who bets a lot of money.
A latin trade is a subset of a latin square which may be replaced with a disjoint mate to obtain a new latin square.
So this is a slight variant on a simple symbol shift, as the p-coordinate is a symbol shift to the right, while the q coordinate is, in effect, a symbol shift to the left.[17] One important feature of this dynamics is that it is measure preserving, so that if X is a subset of the unit square, μ(X) = μ(φ(X)).
Rather than minimize the sum of squared residuals for all data points included in ordinary least squares regression, LTS minimizes the sum of squared residuals for a subset of data points that minimizes the sum of squared residuals to remove the influence of outliers from the regression.
In a subset of geometries, average root mean square geometric differences were 0.54 mm for all structures and in quasi-static analyses over a range of flexion angles, differences in predicted peak contact pressures were less than 5.3% between the semi-automated and manually generated models.
In the rectangle packing problem we are given a set R of rectangles with positive profits and the goal is to pack a subset of R into a unit size square bin [0,1]×[0,1] so that the total profit of the rectangles that are packed is maximized.
As the dataset carries a large amount of information and is in a continental scale, we only select a subset of data, namely, a 10° by 10° square over the state of Colorado and part of its surrounding states, from years 2004 and 2005.
Figure 7A displays a subset of the velocity profiles, with the corresponding least-squares fits.
Thus, a constant shift removing the mean deviation (md) on a subset of samples (the training set) reduces the mean square deviation (msd) on another subset of samples (the validation set).
