def lp(S, A, r, T, s0): """ Solve the LP problem to find out the optimal occupancy Args: S: state set A: action set r: reward T: transition function s0: init state """ m = CPlexModel() if not config.VERBOSE or config.DEBUG: m.setVerbosity(0) # useful constants Sr = range(len(S)) v = m.new(len(S), name='v') for s in Sr: for a in A: m.constrain(v[s] >= r(S[s], a) + sum(v[sp] * T(S[s], a, S[sp]) for sp in Sr)) # obj obj = m.minimize(v[s0]) ret = util.Counter() for s in Sr: ret[S[s]] = m[v][s] return ret
def lp(S, A, r, T, s0): """ Solve the LP problem to find out the optimal occupancy Args: S: state set A: action set r: reward T: transition function s0: init state """ m = CPlexModel() if not config.VERBOSE or config.DEBUG: m.setVerbosity(0) # useful constants Sr = range(len(S)) v = m.new(len(S), name='v') for s in Sr: for a in A: m.constrain(v[s] >= r(S[s], a) + sum(v[sp] * T(S[s], a, S[sp]) for sp in Sr)) # obj obj = m.minimize(v[s0]) ret = util.Counter() for s in Sr: ret[S[s]] = m[v][s] return ret
def rewardUncertainMILP(S, A, R, T, s0, terminal, k, optV, gamma=1): """ The algorithm adapted from Viappiani, Paolo and Boutilier, CraigOptimal. set recommendations based on regret This algorithm would find the minimax-regret policy query in our problem. Not sure how to use this algorithm. """ m = CPlexModel() if not config.VERBOSE: m.setVerbosity(0) M = 100000 # state range Sr = range(len(S)) # action range Ar = range(len(A)) mr = m.new(name='mr') # decision variables x = m.new((k, len(S), len(A)), lb=0, name='x') v = m.new((k, len(R)), name='v') I = m.new((k, len(R)), vtype=bool, name='I') for r in range(len(R)): m.constrain(mr >= sum(v[i, r]) for i in range(k)) for r in range(len(R)): for i in range(k): m.constrain(v[i, r] >= optV[r] - sum(x[i, s, a] * R[r](S[s], A[a]) for s in Sr for a in Ar) + (I[i, r] - 1) * M) # make sure x is a valid occupancy for i in range(k): for sp in Sr: m.constrain( sum(x[i, s, a] * ((s == sp) - gamma * T(S[s], A[a], S[sp])) for s in Sr for a in Ar) == (S[sp] == s0)) for r in range(len(R)): m.constrain(sum(I[i, r] for i in range(k)) == 1) for r in range(len(R)): for i in range(k): m.constrain(v[i, r] >= 0) obj = m.minimize(mr) return obj, m[I]
def rewardUncertainMILP(S, A, R, T, s0, terminal, k, optV, gamma=1): """ The algorithm adapted from Viappiani, Paolo and Boutilier, CraigOptimal. set recommendations based on regret This algorithm would find the minimax-regret policy query in our problem. Not sure how to use this algorithm. """ m = CPlexModel() if not config.VERBOSE: m.setVerbosity(0) M = 100000 # state range Sr = range(len(S)) # action range Ar = range(len(A)) mr = m.new(name='mr') # decision variables x = m.new((k, len(S), len(A)), lb=0, name='x') v = m.new((k, len(R)), name='v') I = m.new((k, len(R)), vtype=bool, name='I') for r in range(len(R)): m.constrain(mr >= sum(v[i, r]) for i in range(k)) for r in range(len(R)): for i in range(k): m.constrain(v[i, r] >= optV[r] - sum(x[i, s, a] * R[r](S[s], A[a]) for s in Sr for a in Ar) + (I[i, r] - 1) * M) # make sure x is a valid occupancy for i in range(k): for sp in Sr: m.constrain(sum(x[i, s, a] * ((s == sp) - gamma * T(S[s], A[a], S[sp])) for s in Sr for a in Ar) == (S[sp] == s0)) for r in range(len(R)): m.constrain(sum(I[i, r] for i in range(k)) == 1) for r in range(len(R)): for i in range(k): m.constrain(v[i, r] >= 0) obj = m.minimize(mr) return obj, m[I]
def solve_GT_LP_relax_noisy_CPLEX(A, y, lambda_reg, do_binary=False): ''' solve the LP formulation of noisy boolean compressed sensing using CPLEX''' M, N = A.shape assert np.linalg.norm(y - y**2) <= 1e-10, "Inputs must be binary" inds_1 = np.where(y == 1)[0] inds_0 = np.where(y == 0)[0] A1 = A[inds_1, :] A0 = A[inds_0, :] ### introduce random small perturbations to avoid degenerate solutions w_pert_x = np.ones(N) + 0.001 * np.random.rand(N) w_pert_xi = np.ones(M) + 0.001 * np.random.rand(M, 1) ### try cplex directy: M1 = len(inds_1) M0 = len(inds_0) w_pert_xi_pos = w_pert_xi[inds_1] w_pert_xi_neg = w_pert_xi[inds_0] m = CPlexModel(verbosity=0) if do_binary: ### solve the binary problem using cuts + branch and bound x_sdp = m.new(N, vtype='bool', lb=0, ub=1) xi_sdp0 = m.new(M0, vtype='bool', lb=0) xi_sdp1 = m.new(M1, vtype='bool', lb=0) else: x_sdp = m.new(N, vtype='real', lb=0, ub=1) xi_sdp0 = m.new(M0, vtype='real', lb=0) xi_sdp1 = m.new(M1, vtype='real', lb=0) m.constrain(A1 * x_sdp + xi_sdp1 >= 1) m.constrain(A0 * x_sdp == 0 + xi_sdp0) value = m.minimize(x_sdp.sum() + lambda_reg * (xi_sdp0.sum() + xi_sdp1.sum())) #m.minimize(w_pert_x*x_sdp + lambda_reg*w_pert_xi*xi_sdp) x_hat = m[x_sdp] xi_hat0 = m[xi_sdp0] xi_hat1 = m[xi_sdp1] xi_hat = np.zeros(M) xi_hat[inds_0] = xi_hat0 xi_hat[inds_1] = xi_hat1 return x_hat, xi_hat
m.constrain(totalSize <= sizeBin) upperBound = sum(sizeItems) m.constrain(totalSize <= binUsed[b] * upperBound) m.constrain(binUsed[b] <= totalSize) pass # each item assigned to exactly one bin for i in range(numItems): numBinsForItem = sum([assign[i,b] for b in range(numBins)]) m.constrain(numBinsForItem == 1) pass numBinsUsed = sum(binUsed) try: m.minimize(numBinsUsed, starting_dict={assign:startAssign}, emphasis=1,\ time_limit=100) pass except Exception, e: logging.exception(e) pass print "Number of constraints: %.1f" % m.getNRows() print "Number of variables: %.1f" % m.getNCols() print "Number of quadratic constraints: %.1f" % m.getNQCs() m.minimize(numBinsUsed, starting_dict={assign:startAssign}, emphasis=1,\ tree_limit=2, work_dir="mapper/output") print m[binUsed] print int(m[numBinsUsed])