Coverage for gpkit/solvers/cvxopt.py: 100%

1"Implements the GPkit interface to CVXOPT" 

2import numpy as np 

3from cvxopt import spmatrix, matrix 

4from cvxopt.solvers import gp 

5from gpkit.exceptions import UnknownInfeasible, DualInfeasible 

6 

7 

8# pylint: disable=too-many-locals,too-many-statements 

9def optimize(*, c, A, k, meq_idxs, use_leqs=True, **kwargs): 

10 """Interface to the CVXOPT solver 

11 

12 Definitions 

13 ----------- 

14 "[a,b] array of floats" indicates array-like data with shape [a,b] 

15 n is the number of monomials in the gp 

16 m is the number of variables in the gp 

17 p is the number of posynomial constraints in the gp 

18 

19 Arguments 

20 --------- 

21 c : floats array of shape n 

22 Coefficients of each monomial 

23 A : floats array of shape (n, m) 

24 Exponents of the various free variables for each monomial. 

25 k : ints array of shape p+1 

26 k[0] is the number of monomials (rows of A) present in the objective 

27 k[1:] is the number of monomials present in each constraint 

28 

29 Returns 

30 ------- 

31 dict 

32 Contains the following keys 

33 "success": bool 

34 "objective_sol" float 

35 Optimal value of the objective 

36 "primal_sol": floats array of size m 

37 Optimal value of free variables. Note: not in logspace. 

38 "dual_sol": floats array of size p 

39 Optimal value of the dual variables, in logspace. 

40 """ 

41 log_c = np.log(np.array(c)) 

42 A = A.tocsr() 

43 maxcol = A.shape[1]-1 

44 lse_mons, lin_mons, leq_mons = [], [], [] 

45 lse_posys, lin_posys, leq_posys = [], [], [] 

46 constraint_hashes = set() 

47 for i, n_monomials in enumerate(k): 

48 start = sum(k[:i]) 

49 mons = range(start, start+k[i]) 

50 A_m = A[mons, :].tocoo() 

51 chash = hash((c[i], tuple(A_m.data), tuple(A_m.row), tuple(A_m.col))) 

52 if chash in constraint_hashes: 

53 continue # already got it 

54 if i: # skip cost posy 

55 constraint_hashes.add(chash) 

56 if use_leqs and start in meq_idxs.all: 

57 if start in meq_idxs.first_half: 

58 leq_posys.append(i) 

59 leq_mons.extend(mons) 

60 elif i != 0 and n_monomials == 1: 

61 lin_mons.extend(mons) 

62 lin_posys.append(i) 

63 else: 

64 lse_mons.extend(mons) 

65 lse_posys.append(i) 

66 if leq_mons: 

67 A_leq = A[leq_mons, :].tocoo() 

68 log_c_leq = log_c[leq_mons] 

69 kwargs["A"] = spmatrix([float(r) for r in A_leq.data]+[0], 

70 [int(r) for r in A_leq.row]+[0], 

71 [int(r) for r in A_leq.col]+[maxcol], tc="d") 

72 kwargs["b"] = matrix(-log_c_leq) 

73 if lin_mons: 

74 A_lin = A[lin_mons, :].tocoo() 

75 log_c_lin = log_c[lin_mons] 

76 kwargs["G"] = spmatrix([float(r) for r in A_lin.data]+[0], 

77 [int(r) for r in A_lin.row]+[0], 

78 [int(r) for r in A_lin.col]+[maxcol], tc="d") 

79 kwargs["h"] = matrix(-log_c_lin) 

80 k_lse = [k[i] for i in lse_posys] 

81 A_lse = A[lse_mons, :].tocoo() 

82 log_c_lse = log_c[lse_mons] 

83 F = spmatrix([float(r) for r in A_lse.data]+[0], 

84 [int(r) for r in A_lse.row]+[0], 

85 [int(r) for r in A_lse.col]+[maxcol], tc="d") 

86 g = matrix(log_c_lse) 

87 try: 

88 solution = gp(k_lse, F, g, **kwargs) 

89 except ValueError as e: 

90 raise DualInfeasible() from e 

91 if solution["status"] != "optimal": 

92 raise UnknownInfeasible("solution status " + repr(solution["status"])) 

93 la = np.zeros(len(k)) 

94 la[lin_posys] = list(solution["zl"]) 

95 la[lse_posys] = [1.] + list(solution["znl"]) 

96 for leq_posy, yi in zip(leq_posys, solution["y"]): 

97 if yi >= 0: 

98 la[leq_posy] = yi 

99 else: # flip it around to the other "inequality" 

100 la[leq_posy+1] = -yi 

101 return dict(status=solution["status"], 

102 objective=np.exp(solution["primal objective"]), 

103 primal=np.ravel(solution["x"]), 

104 la=la)