Weighing matrix

<h2 id="properties">Properties</h2>
Some properties are immediate from the definition. If 
 
 
 
 W
 
 
 {\displaystyle W}
 
 is a 
 
 
 
 W
 (
 n
 ,
 w
 )
 
 
 {\displaystyle W(n,w)}
 
, then:

<ul><li>The rows of 
 
 
 
 W
 
 
 {\displaystyle W}
 
 are pairwise <a href="/facts/Orthogonal/JVIjYPog">orthogonal</a>. Similarly, the columns are pairwise orthogonal.</li>
<li>Each row and each column of 
 
 
 
 W
 
 
 {\displaystyle W}
 
 has exactly 
 
 
 
 w
 
 
 {\displaystyle w}
 
 non-zero elements.</li>
<li>
 
 
 
 
 W
 
 
 T
 
 
 
 W
 =
 w
 I
 
 
 {\displaystyle W^{\mathsf {T}}W=wI}
 
, since the definition means that 
 
 
 
 
 W
 
 −
 1
 
 
 =
 
 w
 
 −
 1
 
 
 
 W
 
 
 T
 
 
 
 
 
 {\displaystyle W^{-1}=w^{-1}W^{\mathsf {T}}}
 
, where 
 
 
 
 
 W
 
 −
 1
 
 
 
 
 {\displaystyle W^{-1}}
 
 is the <a href="/facts/Inverse_matrix/fPqXk3V8">inverse</a> of 
 
 
 
 W
 
 
 {\displaystyle W}
 
.</li>
<li>
 
 
 
 det
 W
 =
 ±
 
 w
 
 n
 
 /
 
 2
 
 
 
 
 {\displaystyle \det W=\pm w^{n/2}}
 
 where 
 
 
 
 det
 W
 
 
 {\displaystyle \det W}
 
 is the <a href="/facts/Determinant/eGYqJ2TF">determinant</a> of 
 
 
 
 W
 
 
 {\displaystyle W}
 
.</li></ul>
A weighing matrix is a generalization of a <a href="/facts/Hadamard_matrix/q56o9lgM">Hadamard matrix</a>, which does not allow zero entries.<a class="footnote-ref" id="fnref:4" href="#fn:4">4</a> As two special cases, a 
 
 
 
 W
 (
 n
 ,
 n
 )
 
 
 {\displaystyle W(n,n)}
 
 is a <a href="/facts/Hadamard_matrix/q56o9lgM">Hadamard matrix</a><a class="footnote-ref" id="fnref:5" href="#fn:5">5</a> and a 
 
 
 
 W
 (
 n
 ,
 n
 −
 1
 )
 
 
 {\displaystyle W(n,n-1)}
 
 is equivalent to a <a href="/facts/Conference_matrix/flsYHR2e">conference matrix</a>.

<h2 id="applications">Applications</h2>
<h3>Experimental design</h3>
See also: <a href="/facts/Design_of_experiments/VMrNftbr">Design of experiments § Example</a>
Weighing matrices take their name from the problem of measuring the weight of multiple objects. If a measuring device has a statistical variance of 
 
 
 
 
 σ
 
 2
 
 
 
 
 {\displaystyle \sigma ^{2}}
 
, then measuring the weights of 
 
 
 
 N
 
 
 {\displaystyle N}
 
 objects and subtracting the (equally imprecise) <a href="/facts/Tare_weight/qI55qpDP">tare weight</a> will result in a final measurement with a variance of 
 
 
 
 2
 
 σ
 
 2
 
 
 
 
 {\displaystyle 2\sigma ^{2}}
 
.<a class="footnote-ref" id="fnref:6" href="#fn:6">6</a> It is possible to increase the accuracy of the estimated weights by measuring different subsets of the objects, especially when using a <a href="/facts/Balance_scale/6Yvv6frI">balance scale</a> where objects can be put on the opposite measuring pan where they subtract their weight from the measurement.
An order 
 
 
 
 n
 
 
 {\displaystyle n}
 
 matrix 
 
 
 
 W
 
 
 {\displaystyle W}
 
 can be used to represent the placement of 
 
 
 
 n
 
 
 {\displaystyle n}
 
 objects—including the tare weight—in 
 
 
 
 n
 
 
 {\displaystyle n}
 
 trials. Suppose the left pan of the balance scale adds to the measurement and the right pan subtracts from the measurement. Each element of this matrix 
 
 
 
 
 w
 
 i
 j
 
 
 
 
 {\displaystyle w_{ij}}
 
 will have:

w
          
            i
            j
          
        
        =
        
          
            {
            
              
                
                  0
                
                
                  
                    if on the 
                  
                  i
                  
                    th trial the 
                  
                  j
                  
                    th object was not measured
                  
                
              
              
                
                  1
                
                
                  
                    if on the 
                  
                  i
                  
                    th trial the 
                  
                  j
                  
                    th object was placed in the left pan
                  
                
              
              
                
                  −
                  1
                
                
                  
                    if on the 
                  
                  i
                  
                    th trial the 
                  
                  j
                  
                    th object was placed in the right pan 
                  
                
              
            
            
          
        
      
    
    {\displaystyle w_{ij}={\begin{cases}0&{\text{if on the }}i{\text{th trial the }}j{\text{th object was not measured}}\\1&{\text{if on the }}i{\text{th trial the }}j{\text{th object was placed in the left pan}}\\-1&{\text{if on the }}i{\text{th trial the }}j{\text{th object was placed in the right pan }}\\\end{cases}}}

Let 
 
 
 
 
 x
 
 
 
 {\displaystyle \mathbf {x} }
 
 be a column vector of the measurements of each of the 
 
 
 
 n
 
 
 {\displaystyle n}
 
 trials, let 
 
 
 
 
 e
 
 
 
 {\displaystyle \mathbf {e} }
 
 be the errors to these measurements each <a href="/facts/Independent_and_identically_distributed/othIRaWt">independent and identically distributed</a> with variance 
 
 
 
 
 σ
 
 2
 
 
 
 
 {\displaystyle \sigma ^{2}}
 
, and let 
 
 
 
 
 y
 
 
 
 {\displaystyle \mathbf {y} }
 
 be a column vector of the true weights of each of the 
 
 
 
 n
 
 
 {\displaystyle n}
 
 objects. Then we have:

x
        
        =
        W
        
          y
        
        +
        
          e
        
      
    
    {\displaystyle \mathbf {x} =W\mathbf {y} +\mathbf {e} }

Assuming that 
 
 
 
 W
 
 
 {\displaystyle W}
 
 is <a href="/facts/Nonsingular_matrix/fPqXk3V8">non-singular</a>, we can use the <a href="/facts/Least_squares/50jIwbxC">method of least-squares</a> to calculate an estimate of the true weights:

y
        
        =
        (
        
          W
          
            T
          
        
        W
        
          )
          
            −
            1
          
        
        W
        
          x
        
      
    
    {\displaystyle \mathbf {y} =(W^{T}W)^{-1}W\mathbf {x} }

The variance of the estimated 
 
 
 
 
 y
 
 
 
 {\displaystyle \mathbf {y} }
 
 vector cannot be lower than 
 
 
 
 
 σ
 
 2
 
 
 
 /
 
 n
 
 
 {\displaystyle \sigma ^{2}/n}
 
, and will be minimum <a href="/facts/If_and_only_if/bYSxGJ66">if and only if</a> 
 
 
 
 W
 
 
 {\displaystyle W}
 
 is a weighing matrix.<a class="footnote-ref" id="fnref:7" href="#fn:7">7</a><a class="footnote-ref" id="fnref:8" href="#fn:8">8</a>

<h3>Optical measurement</h3>

Weighing matrices appear in the <a href="/facts/Engineering/RFVg8H3I">engineering</a> of <a href="/facts/Spectrometer/IqRE8xaQ">spectrometers</a>, image scanners,<a class="footnote-ref" id="fnref:9" href="#fn:9">9</a> and optical multiplexing systems.<a class="footnote-ref" id="fnref:10" href="#fn:10">10</a> The design of these instruments involve an optical mask and two detectors that measure the intensity of light. The mask can either transmit light to the first detector, absorb it, or reflect it toward the second detector. The measurement of the second detector is subtracted from the first, and so these three cases correspond to weighing matrix elements of 1, 0, and −1 respectively. As this is essentially the same measurement problem as in the previous section, the usefulness of weighing matrices also applies.<a class="footnote-ref" id="fnref:11" href="#fn:11">11</a>

<h3>Orthogonal designs</h3>
An orthogonal design of order 
 
 
 
 n
 
 
 {\displaystyle n}
 
 and type 
 
 
 
 (
 
 s
 
 1
 
 
 ,
 …
 ,
 
 s
 
 u
 
 
 )
 
 
 {\displaystyle (s_{1},\dots ,s_{u})}
 
 where 
 
 
 
 
 s
 
 i
 
 
 
 
 {\displaystyle s_{i}}
 
 are positive integers, is an 
 
 
 
 n
 ×
 n
 
 
 {\displaystyle n\times n}
 
 matrix whose entries are in the set 
 
 
 
 {
 0
 ,
 ±
 
 x
 
 1
 
 
 ,
 …
 ,
 ±
 
 x
 
 u
 
 
 }
 
 
 {\displaystyle \{0,\pm x_{1},\dots ,\pm x_{u}\}}
 
, where 
 
 
 
 
 x
 
 i
 
 
 
 
 {\displaystyle x_{i}}
 
 are commuting variables. Additionally, an orthogonal design must satisfy:

X
        
          X
          
            T
          
        
        =
        
          ∑
          
            i
            =
            0
          
          
            u
          
        
        
          s
          
            i
          
        
        
          x
          
            i
          
          
            2
          
        
      
    
    {\displaystyle XX^{T}=\sum _{i=0}^{u}s_{i}x_{i}^{2}}

This constraint is also equivalent to the rows of 
 
 
 
 X
 
 
 {\displaystyle X}
 
 being orthogonal and each row having exactly 
 
 
 
 
 s
 
 i
 
 
 
 
 {\displaystyle s_{i}}
 
 occurrences of 
 
 
 
 
 x
 
 i
 
 
 
 
 {\displaystyle x_{i}}
 
.<a class="footnote-ref" id="fnref:12" href="#fn:12">12</a> An orthogonal design can be denoted as 
 
 
 
 
 O
 D
 
 (
 n
 ;
 
 s
 
 1
 
 
 ,
 …
 ,
 
 s
 
 u
 
 
 )
 
 
 {\displaystyle \mathrm {OD} (n;s_{1},\dots ,s_{u})}
 
.<a class="footnote-ref" id="fnref:13" href="#fn:13">13</a> An orthogonal design of one variable is a weighing matrix, and so the two fields of study are connected.<a class="footnote-ref" id="fnref:14" href="#fn:14">14</a> Because of this connection, new orthogonal designs can be discovered by way of weighing matrices.<a class="footnote-ref" id="fnref:15" href="#fn:15">15</a>

<h2 id="examples">Examples</h2>
Note that when weighing matrices are displayed, the symbol 
 
 
 
 −
 
 
 {\displaystyle -}
 
 is used to represent −1. Here are some examples:
This is a 
 
 
 
 W
 (
 2
 ,
 2
 )
 
 
 {\displaystyle W(2,2)}
 
:

(
            
              
                
                  1
                
                
                  1
                
              
              
                
                  1
                
                
                  −
                
              
            
            )
          
        
      
    
    {\displaystyle {\begin{pmatrix}1&1\\1&-\end{pmatrix}}}

This is a 
 
 
 
 W
 (
 4
 ,
 3
 )
 
 
 {\displaystyle W(4,3)}
 
:

(
            
              
                
                  1
                
                
                  1
                
                
                  1
                
                
                  0
                
              
              
                
                  1
                
                
                  −
                
                
                  0
                
                
                  1
                
              
              
                
                  1
                
                
                  0
                
                
                  −
                
                
                  −
                
              
              
                
                  0
                
                
                  1
                
                
                  −
                
                
                  1
                
              
            
            )
          
        
      
    
    {\displaystyle {\begin{pmatrix}1&1&1&0\\1&-&0&1\\1&0&-&-\\0&1&-&1\end{pmatrix}}}

This is a 
 
 
 
 W
 (
 7
 ,
 4
 )
 
 
 {\displaystyle W(7,4)}
 
:

(
            
              
                
                  1
                
                
                  1
                
                
                  1
                
                
                  1
                
                
                  0
                
                
                  0
                
                
                  0
                
              
              
                
                  1
                
                
                  −
                
                
                  0
                
                
                  0
                
                
                  1
                
                
                  1
                
                
                  0
                
              
              
                
                  1
                
                
                  0
                
                
                  −
                
                
                  0
                
                
                  −
                
                
                  0
                
                
                  1
                
              
              
                
                  1
                
                
                  0
                
                
                  0
                
                
                  −
                
                
                  0
                
                
                  −
                
                
                  −
                
              
              
                
                  0
                
                
                  1
                
                
                  −
                
                
                  0
                
                
                  0
                
                
                  1
                
                
                  −
                
              
              
                
                  0
                
                
                  1
                
                
                  0
                
                
                  −
                
                
                  1
                
                
                  0
                
                
                  1
                
              
              
                
                  0
                
                
                  0
                
                
                  1
                
                
                  −
                
                
                  −
                
                
                  1
                
                
                  0
                
              
            
            )
          
        
      
    
    {\displaystyle {\begin{pmatrix}1&1&1&1&0&0&0\\1&-&0&0&1&1&0\\1&0&-&0&-&0&1\\1&0&0&-&0&-&-\\0&1&-&0&0&1&-\\0&1&0&-&1&0&1\\0&0&1&-&-&1&0\end{pmatrix}}}

Another 
 
 
 
 W
 (
 7
 ,
 4
 )
 
 
 {\displaystyle W(7,4)}
 
:

(
            
              
                
                  −
                
                
                  1
                
                
                  1
                
                
                  0
                
                
                  1
                
                
                  0
                
                
                  0
                
              
              
                
                  0
                
                
                  −
                
                
                  1
                
                
                  1
                
                
                  0
                
                
                  1
                
                
                  0
                
              
              
                
                  0
                
                
                  0
                
                
                  −
                
                
                  1
                
                
                  1
                
                
                  0
                
                
                  1
                
              
              
                
                  1
                
                
                  0
                
                
                  0
                
                
                  −
                
                
                  1
                
                
                  1
                
                
                  0
                
              
              
                
                  0
                
                
                  1
                
                
                  0
                
                
                  0
                
                
                  −
                
                
                  1
                
                
                  1
                
              
              
                
                  1
                
                
                  0
                
                
                  1
                
                
                  0
                
                
                  0
                
                
                  −
                
                
                  1
                
              
              
                
                  1
                
                
                  1
                
                
                  0
                
                
                  1
                
                
                  0
                
                
                  0
                
                
                  −
                
              
            
            )
          
        
      
    
    {\displaystyle {\begin{pmatrix}-&1&1&0&1&0&0\\0&-&1&1&0&1&0\\0&0&-&1&1&0&1\\1&0&0&-&1&1&0\\0&1&0&0&-&1&1\\1&0&1&0&0&-&1\\1&1&0&1&0&0&-\end{pmatrix}}}

Which is <a href="/facts/Circulant_matrix/71ohEKT2">circulant</a>, i.e. each row is a <a href="/facts/Cyclic_permutation/UndoYWpl">cyclic shift</a> of the previous row. Such a matrix is called a 
 
 
 
 C
 W
 (
 n
 ,
 k
 )
 
 
 {\displaystyle CW(n,k)}
 
 and is determined by its first row.
Circulant weighing matrices are of special interest since their algebraic structure makes them easier for classification. Indeed, we know that a circulant weighing matrix of order 
 
 
 
 n
 
 
 {\displaystyle n}
 
 and weight 
 
 
 
 k
 
 
 {\displaystyle k}
 
 must be of <a href="/facts/Square_number/jfakASPK">square</a> weight. So, weights 
 
 
 
 1
 ,
 4
 ,
 9
 ,
 16
 ,
 .
 .
 .
 
 
 {\displaystyle 1,4,9,16,...}
 
 are permissible and weights 
 
 
 
 k
 ≤
 25
 
 
 {\displaystyle k\leq 25}
 
 have been completely classified.<a class="footnote-ref" id="fnref:16" href="#fn:16">16</a>
Two special (and actually, extreme) cases of circulant weighing matrices are (A) circulant Hadamard matrices which are <a href="/facts/Conjecture/oPWUb7SF">conjectured</a> not to exist unless their order is less than 5. This conjecture, the circulant Hadamard conjecture first raised by Ryser, is known to be true for many orders but is still <a href="/facts/Open_problem/VuR6yLsW">open</a>. (B) 
 
 
 
 C
 W
 (
 n
 ,
 k
 )
 
 
 {\displaystyle CW(n,k)}
 
 of weight 
 
 
 
 k
 =
 
 s
 
 2
 
 
 
 
 {\displaystyle k=s^{2}}
 
 and minimal order 
 
 
 
 n
 
 
 {\displaystyle n}
 
 exist if 
 
 
 
 s
 
 
 {\displaystyle s}
 
 is a <a href="/facts/Prime_power/R0vo3uYp">prime power</a> and such a circulant weighing matrix can be obtained by signing the complement of a <a href="/facts/Finite_projective_plane/p6ot8oNv">finite projective plane</a>.
Since all 
 
 
 
 C
 W
 (
 n
 ,
 k
 )
 
 
 {\displaystyle CW(n,k)}
 
 for 
 
 
 
 k
 ≤
 25
 
 
 {\displaystyle k\leq 25}
 
 have been classified, the first open case is 
 
 
 
 C
 W
 (
 105
 ,
 36
 )
 
 
 {\displaystyle CW(105,36)}
 
.
The first open case for a general weighing matrix (certainly not a circulant) is 
 
 
 
 W
 (
 35
 ,
 25
 )
 
 
 {\displaystyle W(35,25)}
 
.

<h2 id="equivalence">Equivalence</h2>
Two weighing matrices are considered to be <a href="/facts/Equivalence_relation/1MQ5rtkW">equivalent</a> if one can be obtained from the other by a series of permutations and negations of the rows and columns of the matrix. The classification of weighing matrices is complete for cases where 
 
 
 
 w
 ≤
 5
 
 
 {\displaystyle w\leq 5}
 
 as well as all cases where 
 
 
 
 n
 ≤
 15
 
 
 {\displaystyle n\leq 15}
 
.<a class="footnote-ref" id="fnref:17" href="#fn:17">17</a> However, very little has been done beyond this with exception to classifying circulant weighing matrices.<a class="footnote-ref" id="fnref:18" href="#fn:18">18</a><a class="footnote-ref" id="fnref:19" href="#fn:19">19</a>

<h2 id="existence">Existence</h2>
One major open question about weighing matrices is their existence: for which values of 
 
 
 
 n
 
 
 {\displaystyle n}
 
 and 
 
 
 
 w
 
 
 {\displaystyle w}
 
 does there exist a 
 
 
 
 W
 (
 n
 ,
 w
 )
 
 
 {\displaystyle W(n,w)}
 
? The following conjectures have been proposed about the existence of 
 
 
 
 W
 (
 n
 ,
 w
 )
 
 
 {\displaystyle W(n,w)}
 
:<a class="footnote-ref" id="fnref:20" href="#fn:20">20</a>

<ol><li>If 
 
 
 
 n
 ≡
 2
 
 
 (
 mod
 
 4
 )
 
 
 
 {\displaystyle n\equiv 2{\pmod {4}}}
 
 then there exists a 
 
 
 
 W
 (
 n
 ,
 w
 )
 
 
 {\displaystyle W(n,w)}
 
 if and only if 
 
 
 
 w
 <
 n
 −
 1
 
 
 {\displaystyle w<n-1}
 
 is the sum of two integer squares.</li>
<li>If 
 
 
 
 n
 ≡
 0
 
 
 (
 mod
 
 4
 )
 
 
 
 {\displaystyle n\equiv 0{\pmod {4}}}
 
 then there exists a 
 
 
 
 W
 (
 n
 ,
 w
 )
 
 
 {\displaystyle W(n,w)}
 
 for each 
 
 
 
 w
 <
 n
 
 
 {\displaystyle w<n}
 
.</li>
<li>If 
 
 
 
 n
 ≡
 4
 
 
 (
 mod
 
 8
 )
 
 
 
 {\displaystyle n\equiv 4{\pmod {8}}}
 
 then there exists an orthogonal design 
 
 
 
 
 O
 D
 
 (
 n
 ;
 1
 ,
 1
 )
 
 
 {\displaystyle \mathrm {OD} (n;1,1)}
 
 for all 
 
 
 
 k
 <
 n
 
 
 {\displaystyle k<n}
 
 where 
 
 
 
 k
 
 
 {\displaystyle k}
 
 is the sum of three integer squares.</li>
<li>If 
 
 
 
 n
 ≡
 0
 
 
 (
 mod
 
 8
 )
 
 
 
 {\displaystyle n\equiv 0{\pmod {8}}}
 
 then there exists an orthogonal design 
 
 
 
 
 O
 D
 
 (
 n
 ;
 1
 ,
 k
 )
 
 
 {\displaystyle \mathrm {OD} (n;1,k)}
 
 for all 
 
 
 
 k
 <
 n
 
 
 {\displaystyle k<n}
 
.</li>
<li>If 
 
 
 
 n
 ≡
 2
 
 
 (
 mod
 
 4
 )
 
 
 
 {\displaystyle n\equiv 2{\pmod {4}}}
 
 then there exists an orthogonal design 
 
 
 
 
 O
 D
 
 (
 n
 ;
 1
 ,
 k
 )
 
 
 {\displaystyle \mathrm {OD} (n;1,k)}
 
 for all 
 
 
 
 k
 <
 n
 −
 1
 
 
 {\displaystyle k<n-1}
 
 such that 
 
 
 
 k
 =
 
 a
 
 2
 
 
 
 
 {\displaystyle k=a^{2}}
 
, 
 
 
 
 a
 
 
 {\displaystyle a}
 
 an integer.</li></ol>
Although the last three conjectures are statements on orthogonal designs, it has been shown that the existence of an orthogonal design 
 
 
 
 
 O
 D
 
 (
 n
 ;
 
 s
 
 1
 
 
 ,
 …
 ,
 
 s
 
 u
 
 
 )
 
 
 {\displaystyle \mathrm {OD} (n;s_{1},\dots ,s_{u})}
 
 is equivalent to the existence of 
 
 
 
 
 X
 
 1
 
 
 ,
 …
 ,
 
 X
 
 u
 
 
 
 
 {\displaystyle X_{1},\dots ,X_{u}}
 
 weighing matrices of order 
 
 
 
 n
 
 
 {\displaystyle n}
 
 where 
 
 
 
 
 X
 
 i
 
 
 
 
 {\displaystyle X_{i}}
 
 has weight 
 
 
 
 
 s
 
 i
 
 
 
 
 {\displaystyle s_{i}}
 
.<a class="footnote-ref" id="fnref:21" href="#fn:21">21</a>
An equally important but often overlooked question about weighing matrices is their enumeration: for a given 
 
 
 
 n
 
 
 {\displaystyle n}
 
 and 
 
 
 
 w
 
 
 {\displaystyle w}
 
, how many 
 
 
 
 W
 (
 n
 ,
 w
 )
 
 
 {\displaystyle W(n,w)}
 
's are there?

<h2 id="references">References</h2>

<ol>
<li id="fn:1">Geramita, Anthony V.; Pullman, Norman J.; Wallis, Jennifer S. (1974). "Families of weighing matrices". Bulletin of the Australian Mathematical Society. 10 (1). Cambridge University Press (CUP): 119–122. doi:10.1017/s0004972700040703. ISSN 0004-9727. S2CID 122560830. <a href="https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1971&context=infopapers" target="_blank">https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1971&context=infopapers</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></li>
<li id="fn:2">Raghavarao, Damaraju (1960). "Some Aspects of Weighing Designs". The Annals of Mathematical Statistics. 31 (4). Institute of Mathematical Statistics: 878–884. doi:10.1214/aoms/1177705664. ISSN 0003-4851. <a href="https://doi.org/10.1214%2Faoms%2F1177705664" target="_blank">https://doi.org/10.1214%2Faoms%2F1177705664</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></li>
<li id="fn:3">Seberry, Jennifer (2017). "Some Algebraic and Combinatorial Non-existence Results". Orthogonal Designs. Cham: Springer International Publishing. pp. 7–17. doi:10.1007/978-3-319-59032-5_2. ISBN 978-3-319-59031-8. <a href="978-3-319-59031-8" target="_blank">978-3-319-59031-8</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></li>
<li id="fn:4">Geramita, Anthony V.; Pullman, Norman J.; Wallis, Jennifer S. (1974). "Families of weighing matrices". Bulletin of the Australian Mathematical Society. 10 (1). Cambridge University Press (CUP): 119–122. doi:10.1017/s0004972700040703. ISSN 0004-9727. S2CID 122560830. <a href="https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1971&context=infopapers" target="_blank">https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1971&context=infopapers</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></li>
<li id="fn:5">Geramita, Anthony V.; Pullman, Norman J.; Wallis, Jennifer S. (1974). "Families of weighing matrices". Bulletin of the Australian Mathematical Society. 10 (1). Cambridge University Press (CUP): 119–122. doi:10.1017/s0004972700040703. ISSN 0004-9727. S2CID 122560830. <a href="https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1971&context=infopapers" target="_blank">https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1971&context=infopapers</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></li>
<li id="fn:6">Raghavarao, Damaraju (1971). "Weighing Designs". Constructions and combinatorial problems in design of experiments. New York: Wiley. pp. 305–308. ISBN 978-0471704850. <a href="978-0471704850" target="_blank">978-0471704850</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></li>
<li id="fn:7">Raghavarao, Damaraju (1971). "Weighing Designs". Constructions and combinatorial problems in design of experiments. New York: Wiley. pp. 305–308. ISBN 978-0471704850. <a href="978-0471704850" target="_blank">978-0471704850</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></li>
<li id="fn:8">Koukouvinos, Christos; Seberry, Jennifer (1997). "Weighing matrices and their applications". Journal of Statistical Planning and Inference. 62 (1). Elsevier BV: 91–101. doi:10.1016/s0378-3758(96)00172-3. ISSN 0378-3758. S2CID 122205953. <a href="https://ro.uow.edu.au/cgi/viewcontent.cgi?article=2156&context=infopapers" target="_blank">https://ro.uow.edu.au/cgi/viewcontent.cgi?article=2156&context=infopapers</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></li>
<li id="fn:9">Sloane, Neil J. A.; Harwit, Martin (1976-01-01). "Masks for Hadamard transform optics, and weighing designs". Applied Optics. 15 (1). The Optical Society: 107–114. Bibcode:1976ApOpt..15..107S. doi:10.1364/ao.15.000107. ISSN 0003-6935. PMID 20155192. <a href="/wiki/Bibcode_(identifier)" target="_blank">/wiki/Bibcode_(identifier)</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></li>
<li id="fn:10">Koukouvinos, Christos; Seberry, Jennifer (1997). "Weighing matrices and their applications". Journal of Statistical Planning and Inference. 62 (1). Elsevier BV: 91–101. doi:10.1016/s0378-3758(96)00172-3. ISSN 0378-3758. S2CID 122205953. <a href="https://ro.uow.edu.au/cgi/viewcontent.cgi?article=2156&context=infopapers" target="_blank">https://ro.uow.edu.au/cgi/viewcontent.cgi?article=2156&context=infopapers</a> <a href="#fnref:10" class="footnote-back-ref">↩</a></li>
<li id="fn:11">Sloane, Neil J. A.; Harwit, Martin (1976-01-01). "Masks for Hadamard transform optics, and weighing designs". Applied Optics. 15 (1). The Optical Society: 107–114. Bibcode:1976ApOpt..15..107S. doi:10.1364/ao.15.000107. ISSN 0003-6935. PMID 20155192. <a href="/wiki/Bibcode_(identifier)" target="_blank">/wiki/Bibcode_(identifier)</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></li>
<li id="fn:12">Geramita, Anthony V.; Seberry, Jennifer (1974). "Orthogonal designs III: weighing matrices". Utilitas Mathematica. <a href="https://ro.uow.edu.au/infopapers/958/" target="_blank">https://ro.uow.edu.au/infopapers/958/</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></li>
<li id="fn:13">Charles J. Colbourn (1996). "Orthogonal Designs". In Colbourn, Charles J. (ed.). CRC Handbook of Combinatorial Designs (1 ed.). Boca Raton: CRC Press. p. 400. doi:10.1201/9781003040897. ISBN 9781003040897. <a href="9781003040897" target="_blank">9781003040897</a> <a href="#fnref:13" class="footnote-back-ref">↩</a></li>
<li id="fn:14">Geramita, Anthony V.; Seberry, Jennifer (1974). "Orthogonal designs III: weighing matrices". Utilitas Mathematica. <a href="https://ro.uow.edu.au/infopapers/958/" target="_blank">https://ro.uow.edu.au/infopapers/958/</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></li>
<li id="fn:15">Kotsireas, Ilias; Koukouvinos, Christos; Seberry, Jennifer (2008). "New orthogonal designs from weighing matrices". Australasian Journal of Combinatorics. 40: 99–104. <a href="https://ro.uow.edu.au/infopapers/3113/" target="_blank">https://ro.uow.edu.au/infopapers/3113/</a> <a href="#fnref:15" class="footnote-back-ref">↩</a></li>
<li id="fn:16">Arasu, K. T.; Gordon, Daniel M.; Zhang, Yiran (2021). "New nonexistence results on circulant weighing matrices". Cryptography and Communications. 13 (5): 775–789. arXiv:1908.08447v3. doi:10.1007/s12095-021-00492-0. MR 4322521. <a href="/wiki/ArXiv_(identifier)" target="_blank">/wiki/ArXiv_(identifier)</a> <a href="#fnref:16" class="footnote-back-ref">↩</a></li>
<li id="fn:17">Harada, Masaaki; Munemasa, Akihiro (2012). "On the classification of weighing matrices and self-orthogonal codes". J. Combin. Designs. 20: 40–57. arXiv:1011.5382. doi:10.1002/jcd.20295. S2CID 1004492. <a href="/wiki/ArXiv_(identifier)" target="_blank">/wiki/ArXiv_(identifier)</a> <a href="#fnref:17" class="footnote-back-ref">↩</a></li>
<li id="fn:18">Ang, Miin Huey; Arasu, K.T.; Lun Ma, Siu; Strassler, Yoseph (2008). "Study of proper circulant weighing matrices with weight 9". Discrete Mathematics. 308 (13): 2802–2809. doi:10.1016/j.disc.2004.12.029. <a href="https://doi.org/10.1016%2Fj.disc.2004.12.029" target="_blank">https://doi.org/10.1016%2Fj.disc.2004.12.029</a> <a href="#fnref:18" class="footnote-back-ref">↩</a></li>
<li id="fn:19">Arasu, K.T.; Hin Leung, Ka; Lun Ma, Siu; Nabavi, Ali; Ray-Chaudhuri, D.K. (2006). "Determination of all possible orders of weight 16 circulant weighing matrices". Finite Fields and Their Applications. 12 (4): 498–538. doi:10.1016/j.ffa.2005.06.009. <a href="https://doi.org/10.1016%2Fj.ffa.2005.06.009" target="_blank">https://doi.org/10.1016%2Fj.ffa.2005.06.009</a> <a href="#fnref:19" class="footnote-back-ref">↩</a></li>
<li id="fn:20">Geramita, Anthony V.; Seberry, Jennifer (1974). "Orthogonal designs III: weighing matrices". Utilitas Mathematica. <a href="https://ro.uow.edu.au/infopapers/958/" target="_blank">https://ro.uow.edu.au/infopapers/958/</a> <a href="#fnref:20" class="footnote-back-ref">↩</a></li>
<li id="fn:21">Geramita, Anthony V.; Seberry, Jennifer (1974). "Orthogonal designs III: weighing matrices". Utilitas Mathematica. <a href="https://ro.uow.edu.au/infopapers/958/" target="_blank">https://ro.uow.edu.au/infopapers/958/</a> <a href="#fnref:21" class="footnote-back-ref">↩</a></li>
</ol>

Weighing matrix open-in-new

Weighing matrix