### Table 6 Unconstrained maximum of the quadratic programming problem.

"... In PAGE 32: ... Now (69) restricted to (i; j) can be rewritten as follows: maximize 8 gt; lt; gt; : ?12 i ? i j ? j gt; Kii Kij Kji Kjj i ? i j ? j +vi( i ? i ) + vj( j ? j) ? quot;( i + i + j + j) subject to ( i ? i ) + ( j ? j) = i; i ; j; j 2 [0; C] (81) Next one has to eliminate j; j by exploiting the summation constraint. Ig- noring terms independent of ( ) i one obtains10 maximize ?12( i ? i )2(Kii + Kjj ? 2Kij) ? quot;( i + i )(1 ? s) +( i ? i )(vi ? vj ? (Kij ? Kjj)) subject to ( ) i 2 [L( ); H( )]: (82) The unconstrained maximum of (82) with respect to i or i can be found in Table6 . Here the shorthand := Kii + Kjj ? 2Kij is used.... ..."

### TABLE I MEMORY REQUIREMENTS FOR OBSERVER AND QUADRATIC PROGRAMMING SOLVER ON DSP.

### Table 4: Numerical results on SDP relaxations of quadratic programs with box constraints. standard conversion completion

2003

"... In PAGE 18: ...minimize 12 0 @ 0 qT 0T q Q O 0 O O 1 A X subject to 0 @ 1 0T 0T 0 O O 0 O O 1 A X = 1; 0 @ 0 0T 0T 0 Eii O 0 O Eii 1 A X = 1 (i = 1; 2; ; n); X 2 S1+2n + 9 gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; = gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; ; : Here Eii 2 Sn denotes the matrix with (i; i)th element one and all others zeros. Table4 compares the three methods applied to this particular class of SDPs. denotes the average number of nonzeros per column of the matrix Q 2 Sn, and the vector q 2 Rn.... ..."

Cited by 16

### Table 6.3: Solving linear and quadratic programs (Pentium Pro, 200 MHz).

1999

Cited by 13

### Table 6.4: Solving randomly generated quadratic programs (Pentium Pro, 200 MHz).

1999

Cited by 13

### Table 6.5: Solving very large quadratic programs (IBM, Power 2, 58 MHz).

1999

Cited by 13

### Table 6.4: Solving randomly generated quadratic programs #28Pentium Pro, 200 MHz#29.

1998