def __solver__(self, p): n = p.n x0 = copy(p.x0) xPrev = x0.copy() xf = x0.copy() xk = x0.copy() p.xk = x0.copy() f0 = p.f(x0) fk = f0 ff = f0 p.fk = fk df0 = p.df(x0) ##################################################################### ## #handling box-bounded problems ## if p.__isNoMoreThanBoxBounded__(): ## for k in range(int(p.maxIter)): ## ## #end of handling box-bounded problems isBB = p.__isNoMoreThanBoxBounded__() ## isBB = 0 H = diag(ones(p.n)) if not p.userProvided.c: p.c = lambda x: array([]) p.dc = lambda x: array([]).reshape(0, p.n) if not p.userProvided.h: p.h = lambda x: array([]) p.dh = lambda x: array([]).reshape(0, p.n) p.use_subproblem = 'QP' #p.use_subproblem = 'LLSP' for k in range(p.maxIter + 4): if isBB: f0 = p.f(xk) df = p.df(xk) direction = -df f1 = p.f(xk + direction) ind_l = direction <= p.lb - xk direction[ind_l] = (p.lb - xk)[ind_l] ind_u = direction >= p.ub - xk direction[ind_u] = (p.ub - xk)[ind_u] ff = p.f(xk + direction) ## print 'f0', f0, 'f1', f1, 'ff', ff else: mr = p.getMaxResidual(xk) if mr > p.contol: mr_grad = p.getMaxConstrGradient(xk) lb = p.lb - xk #- p.contol/2 ub = p.ub - xk #+ p.contol/2 c, dc, h, dh, df = p.c(xk), p.dc(xk), p.h(xk), p.dh(xk), p.df( xk) A, Aeq = vstack((dc, p.A)), vstack((dh, p.Aeq)) b = concatenate((-c, p.b - p.matmult(p.A, xk))) #+ p.contol/2 beq = concatenate((-h, p.beq - p.matmult(p.Aeq, xk))) if b.size != 0: isFinite = isfinite(b) ind = where(isFinite)[0] A, b = A[ind], b[ind] if beq.size != 0: isFinite = isfinite(beq) ind = where(isFinite)[0] Aeq, beq = Aeq[ind], beq[ind] if p.use_subproblem == 'LP': #linear linprob = LP(df, A=A, Aeq=Aeq, b=b, beq=beq, lb=lb, ub=ub) linprob.iprint = -1 r2 = linprob.solve( 'cvxopt_glpk') # TODO: replace lpSolve by autoselect if r2.istop <= 0: p.istop = -12 p.msg = "failed to solve LP subproblem" return elif p.use_subproblem == 'QP': #quadratic qp = QP(H=H, f=df, A=A, Aeq=Aeq, b=b, beq=beq, lb=lb, ub=ub) qp.iprint = -1 r2 = qp.solve( 'cvxopt_qp') # TODO: replace solver by autoselect #r2 = qp.solve('qld') # TODO: replace solver by autoselect if r2.istop <= 0: for i in range(4): if p.debug: p.warn("iter " + str(k) + ": attempt Num " + str(i) + " to solve QP subproblem has failed") #qp.f += 2*N*sum(qp.A,0) A2 = vstack((A, Aeq, -Aeq)) b2 = concatenate( (b, beq, -beq)) + pow(10, i) * p.contol qp = QP(H=H, f=df, A=A2, b=b2, iprint=-5) qp.lb = lb - pow(10, i) * p.contol qp.ub = ub + pow(10, i) * p.contol # I guess lb and ub don't matter here try: r2 = qp.solve( 'cvxopt_qp' ) # TODO: replace solver by autoselect except: r2.istop = -11 if r2.istop > 0: break if r2.istop <= 0: p.istop = -11 p.msg = "failed to solve QP subproblem" return elif p.use_subproblem == 'LLSP': direction_c = getConstrDirection(p, xk, regularization=1e-7) else: p.err('incorrect or unknown subproblem') if isBB: X0 = xk.copy() N = 0 result, newX = chLineSearch(p, X0, direction, N, isBB) elif p.use_subproblem != 'LLSP': duals = r2.duals N = 1.05 * abs(duals).sum() direction = r2.xf X0 = xk.copy() result, newX = chLineSearch(p, X0, direction, N, isBB) else: # case LLSP direction_f = -df p2 = NSP(LLSsubprobF, [0.8, 0.8], ftol=0, gtol=0, xtol=1e-5, iprint=-1) p2.args.f = (xk, direction_f, direction_c, p, 1e20) r_subprob = p2.solve('ralg') alpha = r_subprob.xf newX = xk + alpha[0] * direction_f + alpha[1] * direction_c # dw = (direction_f * direction_c).sum() # cos_phi = dw/p.norm(direction_f)/p.norm(direction_c) # res_0, res_1 = p.getMaxResidual(xk), p.getMaxResidual(xk+1e-1*direction_c) # print cos_phi, res_0-res_1 # res_0 = p.getMaxResidual(xk) # optimConstrPoint = getDirectionOptimPoint(p, p.getMaxResidual, xk, direction_c) # res_1 = p.getMaxResidual(optimConstrPoint) # # maxConstrLimit = p.contol #xk = getDirectionOptimPoint(p, p.f, optimConstrPoint, -optimConstrPoint+xk+direction_f, maxConstrLimit = maxConstrLimit) #print 'res_0', res_0, 'res_1', res_1, 'res_2', p.getMaxResidual(xk) #xk = getDirectionOptimPoint(p, p.f, xk, direction_f, maxConstrLimit) #newX = xk.copy() result = 0 # x_0 = X0.copy() # N = j = 0 # while p.getMaxResidual(x_0) > Residual0 + 0.1*p.contol: # j += 1 # x_0 = xk + 0.75**j * (X0-xk) # X0 = x_0 # result, newX = 0, X0 # print 'newIterResidual = ', p.getMaxResidual(x_0) if result != 0: p.istop = result p.xf = newX return xk = newX.copy() fk = p.f(xk) p.xk, p.fk = copy(xk), copy(fk) #p._df = p.df(xk) #################### p.iterfcn() if p.istop: p.xf = xk p.ff = fk #p._df = g FIXME: implement me return
def __solver__(self, p): n = p.n x0 = copy(p.x0) xPrev = x0.copy() xf = x0.copy() xk = x0.copy() p.xk = x0.copy() f0 = p.f(x0) fk = f0 ff = f0 p.fk = fk df0 = p.df(x0) ##################################################################### ## #handling box-bounded problems ## if p.__isNoMoreThanBoxBounded__(): ## for k in range(int(p.maxIter)): ## ## #end of handling box-bounded problems isBB = p.__isNoMoreThanBoxBounded__() ## isBB = 0 H = diag(ones(p.n)) if not p.userProvided.c: p.c = lambda x : array([]) p.dc = lambda x : array([]).reshape(0, p.n) if not p.userProvided.h: p.h = lambda x : array([]) p.dh = lambda x : array([]).reshape(0, p.n) p.use_subproblem = 'QP' #p.use_subproblem = 'LLSP' for k in range(p.maxIter+4): if isBB: f0 = p.f(xk) df = p.df(xk) direction = -df f1 = p.f(xk+direction) ind_l = direction<=p.lb-xk direction[ind_l] = (p.lb-xk)[ind_l] ind_u = direction>=p.ub-xk direction[ind_u] = (p.ub-xk)[ind_u] ff = p.f(xk + direction) ## print 'f0', f0, 'f1', f1, 'ff', ff else: mr = p.getMaxResidual(xk) if mr > p.contol: mr_grad = p.getMaxConstrGradient(xk) lb = p.lb - xk #- p.contol/2 ub = p.ub - xk #+ p.contol/2 c, dc, h, dh, df = p.c(xk), p.dc(xk), p.h(xk), p.dh(xk), p.df(xk) A, Aeq = vstack((dc, p.A)), vstack((dh, p.Aeq)) b = concatenate((-c, p.b-p.matmult(p.A,xk))) #+ p.contol/2 beq = concatenate((-h, p.beq-p.matmult(p.Aeq,xk))) if b.size != 0: isFinite = isfinite(b) ind = where(isFinite)[0] A, b = A[ind], b[ind] if beq.size != 0: isFinite = isfinite(beq) ind = where(isFinite)[0] Aeq, beq = Aeq[ind], beq[ind] if p.use_subproblem == 'LP': #linear linprob = LP(df, A=A, Aeq=Aeq, b=b, beq=beq, lb=lb, ub=ub) linprob.iprint = -1 r2 = linprob.solve('cvxopt_glpk') # TODO: replace lpSolve by autoselect if r2.istop <= 0: p.istop = -12 p.msg = "failed to solve LP subproblem" return elif p.use_subproblem == 'QP': #quadratic qp = QP(H=H,f=df, A=A, Aeq=Aeq, b=b, beq=beq, lb=lb, ub = ub) qp.iprint = -1 r2 = qp.solve('cvxopt_qp') # TODO: replace solver by autoselect #r2 = qp.solve('qld') # TODO: replace solver by autoselect if r2.istop <= 0: for i in range(4): if p.debug: p.warn("iter " + str(k) + ": attempt Num " + str(i) + " to solve QP subproblem has failed") #qp.f += 2*N*sum(qp.A,0) A2 = vstack((A, Aeq, -Aeq)) b2 = concatenate((b, beq, -beq)) + pow(10,i)*p.contol qp = QP(H=H,f=df, A=A2, b=b2, iprint = -5) qp.lb = lb - pow(10,i)*p.contol qp.ub = ub + pow(10,i)*p.contol # I guess lb and ub don't matter here try: r2 = qp.solve('cvxopt_qp') # TODO: replace solver by autoselect except: r2.istop = -11 if r2.istop > 0: break if r2.istop <= 0: p.istop = -11 p.msg = "failed to solve QP subproblem" return elif p.use_subproblem == 'LLSP': direction_c = getConstrDirection(p, xk, regularization = 1e-7) else: p.err('incorrect or unknown subproblem') if isBB: X0 = xk.copy() N = 0 result, newX = chLineSearch(p, X0, direction, N, isBB) elif p.use_subproblem != 'LLSP': duals = r2.duals N = 1.05*abs(duals).sum() direction = r2.xf X0 = xk.copy() result, newX = chLineSearch(p, X0, direction, N, isBB) else: # case LLSP direction_f = -df p2 = NSP(LLSsubprobF, [0.8, 0.8], ftol=0, gtol=0, xtol = 1e-5, iprint = -1) p2.args.f = (xk, direction_f, direction_c, p, 1e20) r_subprob = p2.solve('ralg') alpha = r_subprob.xf newX = xk + alpha[0]*direction_f + alpha[1]*direction_c # dw = (direction_f * direction_c).sum() # cos_phi = dw/p.norm(direction_f)/p.norm(direction_c) # res_0, res_1 = p.getMaxResidual(xk), p.getMaxResidual(xk+1e-1*direction_c) # print cos_phi, res_0-res_1 # res_0 = p.getMaxResidual(xk) # optimConstrPoint = getDirectionOptimPoint(p, p.getMaxResidual, xk, direction_c) # res_1 = p.getMaxResidual(optimConstrPoint) # # maxConstrLimit = p.contol #xk = getDirectionOptimPoint(p, p.f, optimConstrPoint, -optimConstrPoint+xk+direction_f, maxConstrLimit = maxConstrLimit) #print 'res_0', res_0, 'res_1', res_1, 'res_2', p.getMaxResidual(xk) #xk = getDirectionOptimPoint(p, p.f, xk, direction_f, maxConstrLimit) #newX = xk.copy() result = 0 # x_0 = X0.copy() # N = j = 0 # while p.getMaxResidual(x_0) > Residual0 + 0.1*p.contol: # j += 1 # x_0 = xk + 0.75**j * (X0-xk) # X0 = x_0 # result, newX = 0, X0 # print 'newIterResidual = ', p.getMaxResidual(x_0) if result != 0: p.istop = result p.xf = newX return xk = newX.copy() fk = p.f(xk) p.xk, p.fk = copy(xk), copy(fk) #p._df = p.df(xk) #################### p.iterfcn() if p.istop: p.xf = xk p.ff = fk #p._df = g FIXME: implement me return