print 'Constraint value: %.4f' % (fval)
print 'Time taken to solve PDE: %.3f seconds.\n' % (t3 - t2)
# optimization iteration
it = 0
cont = 1
f0old = f0val
#for k in range(iterations) :
while cont:
it += 1
# make MMA step
t2 = time()
ksmma.mmasub(it,M,N,GEPS,iyfree,xval,xmma,\
xmin,xmax,xold1,xold2,xlow,xupp, \
alfa,beta,a,b,c,y,z,ulam, \
f0val,fval,fmax,df0dx,dfdx, \
p,q,p0,q0,uu,gradf,dsrch,hessf)
ksmma.xupdat(N, it, xmma, xval, xold1, xold2)
t3 = time()
# evaluate everything again
t4 = time()
f0val, fval, df0dx, dfdx, U = SolveStokes(xval, useLS, SSargs)
t5 = time()
f0chg = f0val - f0old
f0chg_pc = 100 * (f0val - f0old) / f0old
f0old = f0val
vio = 100 * (fval - gamma) / gamma
print 'Iteration: %.0f' % (it)
print 'Constraint value: %.4f' % (fval) print 'Time taken to solve PDE: %.3f seconds.\n' % (t3-t2) # optimization iteration it = 0 cont = 1 f0old = f0val #for k in range(iterations) : while cont: it += 1 # make MMA step t2 = time() ksmma.mmasub(it,M,N,GEPS,iyfree,xval,xmma,\ xmin,xmax,xold1,xold2,xlow,xupp, \ alfa,beta,a,b,c,y,z,ulam, \ f0val,fval,fmax,df0dx,dfdx, \ p,q,p0,q0,uu,gradf,dsrch,hessf) ksmma.xupdat(N,it,xmma,xval,xold1,xold2) t3 = time() # evaluate everything again t4 = time() f0val,fval,df0dx,dfdx,U = SolveStokes(xval,useLS,SSargs) t5 = time() f0chg = f0val-f0old f0chg_pc = 100*(f0val-f0old)/f0old f0old = f0val vio = 100*(fval-gamma)/gamma print 'Iteration: %.0f' % (it)
print 'Constraint value: %.4f' % (fval) print 'Time taken to solve PDE: %.3f seconds.\n' % (t3-t2) # optimization iteration it = 0 cont = 1 f0old = f0val #for k in range(iterations) : while cont: it += 1 # make MMA step t2 = time() ksmma.mmasub(it,M,N,GEPS,iyfree,xval,xmma,\ xmin,xmax,xold1,xold2,xlow,xupp, \ alfa,beta,mma_a,mma_b,mma_c,mma_y,mma_z,ulam, \ f0val,fval,fmax,df0dx,dfdx, \ mma_p,mma_q,mma_p0,mma_q0,uu,gradf,dsrch,hessf) ksmma.xupdat(N,it,xmma,xval,xold1,xold2) t3 = time() # evaluate everything again t4 = time() f0val,fval,df0dx,dfdx,U = SolveStokes(xval,useLS,SSargs) t5 = time() f0chg = f0val-f0old f0chg_pc = 100*(f0val-f0old)/f0old f0old = f0val vio = 100*(fval-gamma)/gamma print 'Iteration: %.0f' % (it)
print 'Constraint value: %.4f' % (fval)
print 'Time taken to solve PDE: %.3f seconds.\n' % (t3 - t2)
# optimization iteration
it = 0
cont = 1
f0old = f0val
#for k in range(iterations) :
while cont:
it += 1
# make MMA step
t2 = time()
ksmma.mmasub(it,M,N,GEPS,iyfree,xval,xmma,\
xmin,xmax,xold1,xold2,xlow,xupp, \
alfa,beta,mma_a,mma_b,mma_c,mma_y,mma_z,ulam, \
f0val,fval,fmax,df0dx,dfdx, \
mma_p,mma_q,mma_p0,mma_q0,uu,gradf,dsrch,hessf)
ksmma.xupdat(N, it, xmma, xval, xold1, xold2)
t3 = time()
# evaluate everything again
t4 = time()
f0val, fval, df0dx, dfdx, U = SolveStokes(xval, useLS, SSargs)
t5 = time()
f0chg = f0val - f0old
f0chg_pc = 100 * (f0val - f0old) / f0old
f0old = f0val
vio = 100 * (fval - gamma) / gamma
print 'Iteration: %.0f' % (it)
