CS525 Final Exam Solutions Tuesday May 14, 1996 >> %%%%%%%%%%%%%%%%%%%%%Problem 1 Solution%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% >> Q=[2 -1;-1 1];p=[-3;2];A=[-4 3;5 2];b=[-9; 1]; >> M=[Q -A';A zeros(2,2)];q=[p;-b]; >> H=lemketbl(M,q); z1 z2 z3 z4 1 ------------------------------------------------------- w1= | 2.0000 -1.0000 4.0000 -5.0000 -3.0000 w2= | -1.0000 1.0000 -3.0000 -2.0000 2.0000 w3= | -4.0000 3.0000 0.0000 0.0000 9.0000 w4= | 5.0000 2.0000 0.0000 0.0000 -1.0000 >> H=addcol(H,ones(4,1),'z0',5); z1 z2 z3 z4 z0 1 ------------------------------------------------------------------ w1= | 2.0000 -1.0000 4.0000 -5.0000 1.0000 -3.0000 w2= | -1.0000 1.0000 -3.0000 -2.0000 1.0000 2.0000 w3= | -4.0000 3.0000 0.0000 0.0000 1.0000 9.0000 w4= | 5.0000 2.0000 0.0000 0.0000 1.0000 -1.0000 >> H=ljx(H,1,5); z1 z2 z3 z4 w1 1 ------------------------------------------------------------------ z0= | -2.0000 1.0000 -4.0000 5.0000 1.0000 3.0000 w2= | -3.0000 2.0000 -7.0000 3.0000 1.0000 5.0000 w3= | -6.0000 4.0000 -4.0000 5.0000 1.0000 12.0000 w4= | 3.0000 3.0000 -4.0000 5.0000 1.0000 2.0000 >> H=ljx(H,1,1); z0 z2 z3 z4 w1 1 ------------------------------------------------------------------ z1= | -0.5000 0.5000 -2.0000 2.5000 0.5000 1.5000 w2= | 1.5000 0.5000 -1.0000 -4.5000 -0.5000 0.5000 w3= | 3.0000 1.0000 8.0000 -10.0000 -2.0000 3.0000 w4= | -1.5000 4.5000 -10.0000 12.5000 2.5000 6.5000 >> %Complementary solution: x1=z1=1.5;x2=z2=0 >> %min f(x)=2.25-4.5=-2.25 >> %%%%%%%%%%%%%%%%%%%%%Problem 2 Solution%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% >> A=[1 -1;1 -2];b=[-1;-4];p=[1;1];q=[0.5 ;-1]; >> H=totbl(A,b,p); x1 x2 1 --------------------------------- x3= | 1.0000 -1.0000 1.0000 x4= | 1.0000 -2.0000 4.0000 --------------------------------- z= | 1.0000 1.0000 0.0000 >> H=addrow(H,[q' 0],'z0',3); x1 x2 1 --------------------------------- x3= | 1.0000 -1.0000 1.0000 x4= | 1.0000 -2.0000 4.0000 z0= | 0.5000 -1.0000 0.0000 --------------------------------- z= | 1.0000 1.0000 0.0000 >> %1+0.5t>=0, 1-t>=0 imply optimal tableau for 1>=t>=-2 >> %Unbounded objective from below for -2>t >> H=ljx(H,1,2); x1 x3 1 --------------------------------- x2= | 1.0000 -1.0000 1.0000 x4= | -1.0000 2.0000 2.0000 z0= | -0.5000 1.0000 -1.0000 --------------------------------- z= | 2.0000 -1.0000 1.0000 >> %2-0.5t>=0, -1+t>=0 imply optimal tableau for 4>=t>=1 >> H=ljx(H,2,1); x4 x3 1 --------------------------------- x2= | -1.0000 1.0000 3.0000 x1= | -1.0000 2.0000 2.0000 z0= | 0.5000 0.0000 -2.0000 --------------------------------- z= | -2.0000 3.0000 5.0000 >> %-2+0.5t>=0, 3>=0 imply optimal tableau for t>=4. >> %Summary >> % t<-2 objective unbounded below >> % -2=> % 1=> % 4=> %%%%%%%%%%%%%%%%%%%%%Problem 3 Solution%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% (a) False. Because of the cosequent contradiction if it were true: 0=x'A'u=u'Ax>0 (Alternatively, if statement is true then, max{e'u | A'u=0, u>=0}=infinity, hence the dual: min{0'x | Ax >= e} is infeasible, or Ax>0 has no solution, contradicting the statement.) (b) False. For example, for M=-1, q=-1 LCP is infeasible & hence unsolvable.