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rbeezer committed c7f803e

Removed two forward references to Chapter MD (Aidam Meacham)

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 Edit:  About 130 line-break adjustments, for 5 inch text width at 10pt
 Edit:  Edits, Subsection CB.CELT
 Edit:  Solution SLT.C29, added solution to surjective question (Dan Messenger)
+Edit:  Removed two forward references to Chapter MD (Aidam Meacham)
 Change:  Replaced Definition RR by an informal discussion
 Change:  Definition TSVS renamed as Definition SSVS
 Change:  Proof Technique D: discussion about definitions as nouns, adjectives

src/chapter-R.xml

 </annoacro>
 <annoacro type="theorem" acro="SCB">
 <p>
-Diagonalization back in <acroref type="section" acro="SD" /> was really a change of basis to achieve a diagonal matrix repesentation.  Maybe we should be highlighting the more general <acroref type="theorem" acro="MRCB" /> here, but its overly technical description just isn't as appealing.  However, it will be important in some of the matrix decompostions in <acroref type="chapter" acro="MD" />.
+Diagonalization back in <acroref type="section" acro="SD" /> was really a change of basis to achieve a diagonal matrix repesentation.  Maybe we should be highlighting the more general <acroref type="theorem" acro="MRCB" /> here, but its overly technical description just isn't as appealing.  However, it will be important in some of the matrix decompositions you will encounter in a future course in linear algebra.
 </p>
 </annoacro>
 <annoacro type="theorem" acro="EER">
 <p>
-This theorem, with the companion definition, <acroref type="definition" acro="EELT" />, tells us that eigenvalues, and eigenvectors, are fundamentally a characteristic of linear transformations (not matrices).  If you study matrix decompositions in <acroref type="chapter" acro="MD" /> you will come to appreciate that almost all of a matrix's secrets can be unlocked with knowledge of the eigenvalues and eigenvectors.
+This theorem, with the companion definition, <acroref type="definition" acro="EELT" />, tells us that eigenvalues, and eigenvectors, are fundamentally a characteristic of linear transformations (not matrices).  If you study matrix decompositions in a future course in linear algebra you will come to appreciate that almost all of a matrix's secrets can be unlocked with knowledge of the eigenvalues and eigenvectors.
 </p>
 </annoacro>
 <annoacro type="theorem" acro="OD">