Past exam of the mathematics course of the University of Cambridge 2002 iii Paper 30 2 Solution Created 2026-10-03 Updated 2026-10-14
For a binary linear code , define the linear functional . This is the parity of the Hamming weight, so the even-weight subcode of a binary linear code isIf , then . Otherwise its image is all of , and rank-nullity theorem gives . The kernel of a linear map is a linear subspace, proving the required dichotomy including closure under addition.
Let be a full-rank generator matrix. Messages correspond bijectively to codewords . If the th column is nonzero, the coordinate is a nonzero linear functional. Choose with . Translation pairs the messages with coordinate zero and those with coordinate one, so exactly codewords contribute one at position . Summing over the columns proves the total weight of a full-support binary linear code formulaA zero column would contribute zero instead, explaining the hypothesis.
For the Hamming code, use a parity-check matrix whose columns are all nonzero vectors of , once each. Then and . The sum of all columns is zero: each coordinate occurs as one exactly times, which is even for . ConsequentlyUnder this column labelling, the support of a vector of a codeword is identified with a set of distinct nonzero columns whose sum is zero. No one-column or two-column support of a vector has this property, confirming .
We can count the low-weight coefficients of a binary Hamming code directly. For weight three, choose ordered distinct nonzero columns in ways. The third must be , which is nonzero and different from both. Every unordered support of a vector has orderings, so .
For weight four, the first two columns are again distinct and nonzero. The third must avoid , giving possibilities. The fourth is ; it is nonzero because , and cannot equal any of the first three because those are distinct. Conversely every ordered zero-sum four-tuple arises in this way. Dividing by gives .
For weight five, the first three columns must be linearly independent. If , the sum condition would force the fourth and fifth to coincide. Thus there are choices for the first three. Put . The fourth column must avoid all seven nonzero vectors of their linear span: would repeat a column; would force the fifth column to be zero; and would force the fifth column to be , respectively. Conversely, if avoids these seven vectors, is nonzero and distinct from . This gives choices for and determines . Dividing by yieldsAt , the last count is zero, as the argument also shows.
The dual code is the row space of , so its codewords are for . For nonzero , the linear functional takes value one on exactly half the vectors of , by the same translation-pairing argument used above. Removing the zero vector removes only a zero value, so the Hamming weight is still . Since has matrix rank , distinct give distinct codewords. This proves the constant weight of a binary simplex code and its full distribution:In particular, the ordinary weight enumerator of the binary simplex code is .
Apply the MacWilliams identity to this dual code, whose size is . For , the result isThe coefficient of in the second product is a value of the binary Kravchuk polynomialsThese bounds merely enforce and . Since , they agree with the bounds in the PDF. Therefore the requested formula isFor a further check on the low-weight counts, givesThus , , and . Substituting these into the boxed formula and using reproduces the three direct combinatorial counts above.
Finally, at the only possible term in the defining sum has , so , because is even. ConsequentlyThe unique length- binary word of weight is the all-ones word, exactly as proved by the parity-check matrix calculation.