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| From: | cen zhihao |
| Subject: | [Help-glpk] dual result problem |
| Date: | Tue, 31 Mar 2009 21:52:09 +0300 |
I have a question of dual result : the one by glpsol and the one by glp_simplex are not the same. code c++ : glp_prob* probCopy= glp_create_prob(); glp_copy_prob(probCopy, xxxxxx, GLP_OFF); glp_simplex(probCopy, xxxxxx); glp_write_lp(probCopy, NULL, "prob.lp"); lpx_print_sol(probCopy, "result_simplex.txt"); and glpsol --cpxlp prob.lp -o result_glpsol.txt. Thanks in advance. And here are the different results ===================="result_glpsol.txt"====================== Problem: Rows: 19 Columns: 40 Non-zeros: 116 Status: OPTIMAL Objective: obj = 3.135276971 (MINimum) No. Row name St Activity Lower bound Upper bound Marginal ------ ------------ -- ------------- ------------- ------------- ------------- 1 r_1 NS 48 48 = -0.698933 2 r_2 NS 22.4 22.4 = -0.668988 3 r_3 NS 98.8 98.8 = -0.365573 4 r_4 NS 12.8 12.8 = 1.25434 5 r_5 B 14.45 14.45 = 6 r_6 B 31.71 31.71 = 7 r_7 B 68 -36.6 8 r_8 B 0 0 9 r_9 B 0 0 10 r_10 B 14.63 0 11 r.30 NU 14.63 14.63 -0.113469 12 r_11 B 7.97 0 13 r.32 NU 7.97 7.97 -2.19142 14 r_12 B 32.44 0 15 r.34 B 32.44 1e+020 16 r_13 B 19.04 0 17 r_14 NL 36 36 0.764259 18 r.39 B 36 50 19 r_15 B 3.2 3.2 No. Column name St Activity Lower bound Upper bound Marginal ------ ------------ -- ------------- ------------- ------------- ------------- 1 x_1 NL 0 0 0.893658 2 x_2 NL 0 0 0.0875705 3 x_3 NL 0 0 0.666719 4 x_4 NL 0 0 2.55399 5 x_5 NL 0 0 0.833768 6 x_6 B 3.2 0 7 x_7 NL 0 0 0.606829 8 x_8 NL 0 0 0.226939 9 x_9 B 3.2 0 10 x_10 NL 0 0 0.211116 11 x_11 NL 0 0 0.626995 12 x_12 NL 0 0 < eps 13 x_13 NL 0 0 3.10799 14 x_14 NL 0 0 2.481 15 x_15 NL 0 0 0.626995 16 x_16 B 0 0 17 x_17 NL 0 0 0.626995 18 x_18 NL 0 0 < eps 19 x_19 NL 0 0 0.626995 20 x_20 B 0 0 21 x_21 NL 0 0 0.626995 22 x_22 B 0 0 23 x_23 NL 0 0 0.626995 24 x_24 B 0 0 25 x_25 NL 0 0 0.626995 26 x_26 B 6.1 0 27 x_27 NS 48 48 = 0.698933 28 x_28 NS 19.2 19.2 = 0.668988 29 x_29 NS 89.5 89.5 = 0.365573 30 x_30 NS 9.6 9.6 = -1.25434 31 x_31 NS 14.45 14.45 = < eps 32 x_32 NS 28.51 28.51 = < eps 33 x_33 NS 64.8 64.8 = < eps 34 x_34 NS 0 0 = < eps 35 x_35 NS 0 0 = < eps 36 x_36 NS 14.63 14.63 = 0.113469 37 x_37 NS 7.97 7.97 = 2.19142 38 x_38 NS 26.34 26.34 = < eps 39 x_39 NS 19.04 19.04 = < eps 40 x_40 NS 29.9 29.9 = -0.764259 Karush-Kuhn-Tucker optimality conditions: KKT.PE: max.abs.err. = 1.42e-014 on row 7 max.rel.err. = 2.06e-016 on row 7 High quality KKT.PB: max.abs.err. = 4.44e-015 on row 19 max.rel.err. = 1.06e-015 on row 19 High quality KKT.DE: max.abs.err. = 3.33e-016 on column 14 max.rel.err. = 1.31e-016 on column 4 High quality KKT.DB: max.abs.err. = 0.00e+000 on row 0 max.rel.err. = 0.00e+000 on row 0 High quality End of output ================="result_simplex.txt"================== Problem: Rows: 16 Columns: 40 Non-zeros: 88 Status: OPTIMAL Objective: 3.135276971 (MINimum) No. Row name St Activity Lower bound Upper bound Marginal ------ ------------ -- ------------- ------------- ------------- ------------- 1 NS 48 48 = -2.80279 2 NS 22.4 22.4 = -1.60607 3 NS 98.8 98.8 = -0.365573 4 B 12.8 12.8 = 5 B 14.45 14.45 = 6 NS 31.71 31.71 = 2.19142 7 B 68 -36.6 8 B 0 0 9 B 0 0 10 NU 14.63 0 14.63 -0.113469 11 B 7.97 0 7.97 12 B 32.44 0 1e+20 13 B 19.04 0 14 NL 36 36 50 0.764259 15 B 3.2 3.2 16 B 0 No. Column name St Activity Lower bound Upper bound Marginal ------ ------------ -- ------------- ------------- ------------- ------------- 1 NL 0 0 2.99751 2 B 0 0 3 NL 0 0 2.77057 4 NL 0 0 4.65784 5 NL 0 0 1.77085 6 B 3.2 0 7 NL 0 0 1.54391 8 NL 0 0 0.226939 9 B 3.2 0 10 NL 0 0 0.211116 11 NL 0 0 2.73085 12 NL 0 0 2.10385 13 NL 0 0 3.02042 14 NL 0 0 2.39343 15 NL 0 0 2.73085 16 NL 0 0 2.10385 17 NL 0 0 1.56408 18 NL 0 0 0.937085 19 NL 0 0 0.626995 20 B 0 0 21 NL 0 0 1.56408 22 NL 0 0 0.937085 23 NL 0 0 0.626995 24 B 0 0 25 NL 0 0 0.626995 26 B 6.1 0 27 NS 48 48 = 2.80279 28 NS 19.2 19.2 = 1.60607 29 NS 89.5 89.5 = 0.365573 30 NS 9.6 9.6 = < eps 31 NS 14.45 14.45 = < eps 32 NS 28.51 28.51 = -2.19142 33 NS 64.8 64.8 = < eps 34 NS 0 0 = < eps 35 NS 0 0 = < eps 36 NS 14.63 14.63 = 0.113469 37 NS 7.97 7.97 = < eps 38 NS 26.34 26.34 = < eps 39 NS 19.04 19.04 = < eps 40 NS 29.9 29.9 = -0.764259 Karush-Kuhn-Tucker optimality conditions: KKT.PE: max.abs.err. = 7.11e-15 on row 7 max.rel.err. = 1.06e-16 on row 12 High quality KKT.PB: max.abs.err. = 4.44e-15 on row 15 max.rel.err. = 1.06e-15 on row 15 High quality KKT.DE: max.abs.err. = 8.88e-16 on column 4 max.rel.err. = 2.34e-16 on column 4 High quality KKT.DB: max.abs.err. = 0.00e+00 on row 0 max.rel.err. = 0.00e+00 on row 0 High quality End of output ================="prob.lp"==================== \* Problem: Unknown *\ Minimize obj: + 0.194725365844387 x_1 - 0.611362153117218 x_2 - 0.0322134773543005 x_3 + 1.85505584946619 x_4 + 0.16478029267551 x_5 + 0.585352152962554 x_6 - 0.062158550523178 x_7 - 0.138634202092711 x_8 - 0.365573045291399 x_9 - 0.154457366081977 x_10 - 0.185407046010089 x_11 - 0.04814311635353 x_12 + 0.217636713470713 x_13 + 0.354900643127272 x_14 - 0.0719376244107455 x_15 + 0.0653263052458138 x_16 - 0.155461972841212 x_17 - 0.0181980431846525 x_18 - 0.979077592609059 x_19 - 0.841813662952499 x_20 - 0.041992551241868 x_21 + 0.0952713784146914 x_22 + 0.147952521927009 x_23 + 0.285216451583569 x_24 + 0.261421943526353 x_25 + 0.398685873182913 x_26 Subject To r_1: + x_27 + x_16 + x_15 + x_14 + x_13 + x_12 + x_11 + x_4 + x_3 + x_2 + x_1 = 48 r_2: + x_28 + x_22 + x_21 + x_20 + x_19 + x_18 + x_17 + x_7 + x_6 + x_5 = 22.4 r_3: + x_29 + x_26 + x_25 + x_24 + x_23 + x_10 + x_9 + x_8 = 98.8 r_4: + x_30 + x_20 + x_19 + x_6 = 12.8 r_5: + x_31 + x_8 + x_5 + x_1 = 14.45 r_6: + x_32 + x_6 + x_2 = 31.71 r_7: + x_33 + x_9 + x_7 + x_3>= -36.6 r_8: + x_34 + x_4>= 0 r_9: + x_35 + x_10>= 0 r_10: + x_36 + x_24 + x_23 + x_18 + x_17 + x_12 + x_11>= 0 + x_36 + x_24 + x_23 + x_18 + x_17 + x_12 + x_11 <= 14.63 r_11: + x_37 + x_20 + x_19 + x_14 + x_13>= 0 + x_37 + x_20 + x_19 + x_14 + x_13 <= 7.97 r_12: + x_38 + x_26 + x_25 + x_22 + x_21 + x_16 + x_15>= 0 + x_38 + x_26 + x_25 + x_22 + x_21 + x_16 + x_15 <= 1e+20 r_13: + x_39 + x_25 + x_23 + x_21 + x_19 + x_17 + x_15 + x_13 + x_11 >= 0 r_14: + x_40 + x_26 + x_24 + x_22 + x_20 + x_18 + x_16 + x_14 + x_12 >= 36 + x_40 + x_26 + x_24 + x_22 + x_20 + x_18 + x_16 + x_14 + x_12 <= 50 r_15: + x_9 + x_7 + x_3>= 3.2 Bounds x_27 = 48 x_28 = 19.2 x_29 = 89.5 x_30 = 9.6 x_31 = 14.45 x_32 = 28.51 x_33 = 64.8 x_34 = 0 x_35 = 0 x_36 = 14.63 x_37 = 7.97 x_38 = 26.34 x_39 = 19.04 x_40 = 29.9 End Souhaitez vous « кtre au bureau sans y кtre » ? Oui je le veux !
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I have a question of dual result : the one by glpsol and the one by glp_simplex are not the same. code c++ : glp_prob* probCopy= glp_create_prob(); glp_copy_prob(probCopy, xxxxxx, GLP_OFF); glp_simplex(probCopy, xxxxxx); glp_write_lp(probCopy, NULL, "prob.lp"); lpx_print_sol(probCopy, "result_simplex.txt"); and glpsol --cpxlp prob.lp -o result_glpsol.txt. Thanks in advance. And here are the different results ===================="result_glpsol.txt"====================== Problem: Rows: 19 Columns: 40 Non-zeros: 116 Status: OPTIMAL Objective: obj = 3.135276971 (MINimum) No. Row name St Activity Lower bound Upper bound Marginal ------ ------------ -- ------------- ------------- ------------- ------------- 1 r_1 NS 48 48 = -0.698933 2 r_2 NS 22.4 22.4 = -0.668988 3 r_3 NS 98.8 98.8 = -0.365573 4 r_4 NS 12.8 12.8 = 1.25434 5 r_5 B 14.45 14.45 = 6 r_6 B 31.71 31.71 = 7 r_7 B 68 -36.6 8 r_8 B 0 0 9 r_9 B 0 0 10 r_10 B 14.63 0 11 r.30 NU 14.63 14.63 -0.113469 12 r_11 B 7.97 0 13 r.32 NU 7.97 7.97 -2.19142 14 r_12 B 32.44 0 15 r.34 B 32.44 1e+020 16 r_13 B 19.04 0 17 r_14 NL 36 36 0.764259 18 r.39 B 36 50 19 r_15 B 3.2 3.2 No. Column name St Activity Lower bound Upper bound Marginal ------ ------------ -- ------------- ------------- ------------- ------------- 1 x_1 NL 0 0 0.893658 2 x_2 NL 0 0 0.0875705 3 x_3 NL 0 0 0.666719 4 x_4 NL 0 0 2.55399 5 x_5 NL 0 0 0.833768 6 x_6 B 3.2 0 7 x_7 NL 0 0 0.606829 8 x_8 NL 0 0 0.226939 9 x_9 B 3.2 0 10 x_10 NL 0 0 0.211116 11 x_11 NL 0 0 0.626995 12 x_12 NL 0 0 < eps 13 x_13 NL 0 0 3.10799 14 x_14 NL 0 0 2.481 15 x_15 NL 0 0 0.626995 16 x_16 B 0 0 17 x_17 NL 0 0 0.626995 18 x_18 NL 0 0 < eps 19 x_19 NL 0 0 0.626995 20 x_20 B 0 0 21 x_21 NL 0 0 0.626995 22 x_22 B 0 0 23 x_23 NL 0 0 0.626995 24 x_24 B 0 0 25 x_25 NL 0 0 0.626995 26 x_26 B 6.1 0 27 x_27 NS 48 48 = 0.698933 28 x_28 NS 19.2 19.2 = 0.668988 29 x_29 NS 89.5 89.5 = 0.365573 30 x_30 NS 9.6 9.6 = -1.25434 31 x_31 NS 14.45 14.45 = < eps 32 x_32 NS 28.51 28.51 = < eps 33 x_33 NS 64.8 64.8 = < eps 34 x_34 NS 0 0 = < eps 35 x_35 NS 0 0 = < eps 36 x_36 NS 14.63 14.63 = 0.113469 37 x_37 NS 7.97 7.97 = 2.19142 38 x_38 NS 26.34 26.34 = < eps 39 x_39 NS 19.04 19.04 = < eps 40 x_40 NS 29.9 29.9 = -0.764259 Karush-Kuhn-Tucker optimality conditions: KKT.PE: max.abs.err. = 1.42e-014 on row 7 max.rel.err. = 2.06e-016 on row 7 High quality KKT.PB: max.abs.err. = 4.44e-015 on row 19 max.rel.err. = 1.06e-015 on row 19 High quality KKT.DE: max.abs.err. = 3.33e-016 on column 14 max.rel.err. = 1.31e-016 on column 4 High quality KKT.DB: max.abs.err. = 0.00e+000 on row 0 max.rel.err. = 0.00e+000 on row 0 High quality End of output ================="result_simplex.txt"================== Problem: Rows: 16 Columns: 40 Non-zeros: 88 Status: OPTIMAL Objective: 3.135276971 (MINimum) No. Row name St Activity Lower bound Upper bound Marginal ------ ------------ -- ------------- ------------- ------------- ------------- 1 NS 48 48 = -2.80279 2 NS 22.4 22.4 = -1.60607 3 NS 98.8 98.8 = -0.365573 4 B 12.8 12.8 = 5 B 14.45 14.45 = 6 NS 31.71 31.71 = 2.19142 7 B 68 -36.6 8 B 0 0 9 B 0 0 10 NU 14.63 0 14.63 -0.113469 11 B 7.97 0 7.97 12 B 32.44 0 1e+20 13 B 19.04 0 14 NL 36 36 50 0.764259 15 B 3.2 3.2 16 B 0 No. Column name St Activity Lower bound Upper bound Marginal ------ ------------ -- ------------- ------------- ------------- ------------- 1 NL 0 0 2.99751 2 B 0 0 3 NL 0 0 2.77057 4 NL 0 0 4.65784 5 NL 0 0 1.77085 6 B 3.2 0 7 NL 0 0 1.54391 8 NL 0 0 0.226939 9 B 3.2 0 10 NL 0 0 0.211116 11 NL 0 0 2.73085 12 NL 0 0 2.10385 13 NL 0 0 3.02042 14 NL 0 0 2.39343 15 NL 0 0 2.73085 16 NL 0 0 2.10385 17 NL 0 0 1.56408 18 NL 0 0 0.937085 19 NL 0 0 0.626995 20 B 0 0 21 NL 0 0 1.56408 22 NL 0 0 0.937085 23 NL 0 0 0.626995 24 B 0 0 25 NL 0 0 0.626995 26 B 6.1 0 27 NS 48 48 = 2.80279 28 NS 19.2 19.2 = 1.60607 29 NS 89.5 89.5 = 0.365573 30 NS 9.6 9.6 = < eps 31 NS 14.45 14.45 = < eps 32 NS 28.51 28.51 = -2.19142 33 NS 64.8 64.8 = < eps 34 NS 0 0 = < eps 35 NS 0 0 = < eps 36 NS 14.63 14.63 = 0.113469 37 NS 7.97 7.97 = < eps 38 NS 26.34 26.34 = < eps 39 NS 19.04 19.04 = < eps 40 NS 29.9 29.9 = -0.764259 Karush-Kuhn-Tucker optimality conditions: KKT.PE: max.abs.err. = 7.11e-15 on row 7 max.rel.err. = 1.06e-16 on row 12 High quality KKT.PB: max.abs.err. = 4.44e-15 on row 15 max.rel.err. = 1.06e-15 on row 15 High quality KKT.DE: max.abs.err. = 8.88e-16 on column 4 max.rel.err. = 2.34e-16 on column 4 High quality KKT.DB: max.abs.err. = 0.00e+00 on row 0 max.rel.err. = 0.00e+00 on row 0 High quality End of output ================="prob.lp"==================== \* Problem: Unknown *\ Minimize obj: + 0.194725365844387 x_1 - 0.611362153117218 x_2 - 0.0322134773543005 x_3 + 1.85505584946619 x_4 + 0.16478029267551 x_5 + 0.585352152962554 x_6 - 0.062158550523178 x_7 - 0.138634202092711 x_8 - 0.365573045291399 x_9 - 0.154457366081977 x_10 - 0.185407046010089 x_11 - 0.04814311635353 x_12 + 0.217636713470713 x_13 + 0.354900643127272 x_14 - 0.0719376244107455 x_15 + 0.0653263052458138 x_16 - 0.155461972841212 x_17 - 0.0181980431846525 x_18 - 0.979077592609059 x_19 - 0.841813662952499 x_20 - 0.041992551241868 x_21 + 0.0952713784146914 x_22 + 0.147952521927009 x_23 + 0.285216451583569 x_24 + 0.261421943526353 x_25 + 0.398685873182913 x_26 Subject To r_1: + x_27 + x_16 + x_15 + x_14 + x_13 + x_12 + x_11 + x_4 + x_3 + x_2 + x_1 = 48 r_2: + x_28 + x_22 + x_21 + x_20 + x_19 + x_18 + x_17 + x_7 + x_6 + x_5 = 22.4 r_3: + x_29 + x_26 + x_25 + x_24 + x_23 + x_10 + x_9 + x_8 = 98.8 r_4: + x_30 + x_20 + x_19 + x_6 = 12.8 r_5: + x_31 + x_8 + x_5 + x_1 = 14.45 r_6: + x_32 + x_6 + x_2 = 31.71 r_7: + x_33 + x_9 + x_7 + x_3>= -36.6 r_8: + x_34 + x_4>= 0 r_9: + x_35 + x_10>= 0 r_10: + x_36 + x_24 + x_23 + x_18 + x_17 + x_12 + x_11>= 0 + x_36 + x_24 + x_23 + x_18 + x_17 + x_12 + x_11 <= 14.63 r_11: + x_37 + x_20 + x_19 + x_14 + x_13>= 0 + x_37 + x_20 + x_19 + x_14 + x_13 <= 7.97 r_12: + x_38 + x_26 + x_25 + x_22 + x_21 + x_16 + x_15>= 0 + x_38 + x_26 + x_25 + x_22 + x_21 + x_16 + x_15 <= 1e+20 r_13: + x_39 + x_25 + x_23 + x_21 + x_19 + x_17 + x_15 + x_13 + x_11 >= 0 r_14: + x_40 + x_26 + x_24 + x_22 + x_20 + x_18 + x_16 + x_14 + x_12 >= 36 + x_40 + x_26 + x_24 + x_22 + x_20 + x_18 + x_16 + x_14 + x_12 <= 50 r_15: + x_9 + x_7 + x_3>= 3.2 Bounds x_27 = 48 x_28 = 19.2 x_29 = 89.5 x_30 = 9.6 x_31 = 14.45 x_32 = 28.51 x_33 = 64.8 x_34 = 0 x_35 = 0 x_36 = 14.63 x_37 = 7.97 x_38 = 26.34 x_39 = 19.04 x_40 = 29.9 End Souhaitez vous « être au bureau sans y être » ? Oui je le veux ! |
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