Steve Jacobsen (jacobsen@ee.ucla.edu), Khosrow Moshirvaziri (moshir@ee.ucla.edu) LINEAR REVERSE CONVEX MINIMIZATION PROBLEM (m=11, n=20): min cx Ax < b x > 0 g(x) < 0 where g(x) = -x'diag(lambda)x + d'x + g0 A,b = -6 1 1 -3 -9 -7 -0 -1 3 7 1 7 4 -2 -2 3 8 -3 -6 -1 5 -9 3 -8 3 3 -0 -5 9 5 -2 6 -7 9 -0 -7 -7 -1 -5 4 9 8 4 -10 3 5 8 8 -8 -9 5 7 -9 -6 -5 -7 1 3 -7 -7 8 3 -5 4 -2 -2 10 -5 8 9 5 10 -5 1 4 -6 2 -5 2 9 6 -5 -1 49 9 -9 4 -3 -1 -9 -9 5 8 -2 -0 -7 -4 7 -0 6 -2 -1 7 6 17 -2 -2 8 -5 5 8 0 7 -5 1 9 -8 8 2 -1 -5 -7 -3 -1 4 22 0 4 5 10 -0 0 -2 -7 -4 -1 5 -5 3 9 9 -0 8 -1 0 4 46 7 2 -5 4 -5 0 -4 -10 -3 -4 1 -10 -7 1 -7 -2 -8 6 2 10 -23 -9 9 -9 5 -5 -4 8 4 0 -6 8 -2 4 -7 -6 -6 -7 9 6 9 11 3 5 5 1 4 6 9 5 7 9 7 8 7 7 0 9 5 5 1 7 1210 22 -1 -8 11 33 25 9 -5 -13 -7 -10 -17 -4 7 17 -7 -30 19 15 -14 1000 c= 3 4 -6 8 7 8 1 -7 -1 10 -6 -1 -4 0 8 -1 -1 6 -3 -6 g0=-2.155057371324785e+06 d= -2.399960259093519e+04 -3.063051000562841e+04 4.516691619612435e+04 -6.363962102013778e+04 -5.552679856924290e+04 -6.457757301484958e+04 -4.091129311573360e+03 4.989903746993247e+04 6.807671758992243e+03 -6.654693278113627e+04 4.228143865864549e+04 1.442372267359577e+04 2.773263583258165e+04 -2.041349733862419e+03 -5.921548811976219e+04 1.047989704974346e+04 8.487120281488647e+03 -4.504303590315735e+04 2.020356006623618e+04 3.794902009191595e+04 lambda = 3 1 7 7 9 4 6 8 1 1 6 7 1 4 2 5 7 6 9 9 optimal x = x(1) = 6.001595738074214e+00 x(2) = 1.368424831776637e+01 x(3) = 2.590684011359048e+01 x(5) = 3.033497749100739e+01 x(6) = 2.533511088613893e+00 x(8) = 1.306117707430680e+01 x(12) = 5.676470129247394e+01 x(17) = 1.119824408808392e+01 x(19) = 2.414352896358711e+01 x(20) = 3.684864985970695e+01 other x's = 0.0