MINLPLib
A Library of Mixed-Integer and Continuous Nonlinear Programming Instances
Home // Instances // Documentation // Download // Statistics
Instance waternd_shamir
Formatsⓘ | ams gms osil py |
Primal Bounds (infeas ≤ 1e-08)ⓘ | |
Other points (infeas > 1e-08)ⓘ | |
Dual Boundsⓘ | 419000.00000000 (LINDO) 419000.00000000 (SCIP) |
Referencesⓘ | D'Ambrosio, Claudia, Bragalli, Cristiana, Lee, Jon, Lodi, Andrea, and Toth, Paolo, Optimal Design of Water Distribution Networks, 2011. Bragalli, Cristiana, D'Ambrosio, Claudia, Lee, Jon, Lodi, Andrea, and Toth, Paolo, On the optimal design of water distribution networks: a practical MINLP approach, Optimization and Engineering, 13, 2012, 219-246. |
Sourceⓘ | water.gms from minlp.org model 134 |
Applicationⓘ | Water Network Design |
Added to libraryⓘ | 25 Sep 2013 |
Problem typeⓘ | MBNLP |
#Variablesⓘ | 135 |
#Binary Variablesⓘ | 112 |
#Integer Variablesⓘ | 0 |
#Nonlinear Variablesⓘ | 23 |
#Nonlinear Binary Variablesⓘ | 0 |
#Nonlinear Integer Variablesⓘ | 0 |
Objective Senseⓘ | min |
Objective typeⓘ | linear |
Objective curvatureⓘ | linear |
#Nonzeros in Objectiveⓘ | 112 |
#Nonlinear Nonzeros in Objectiveⓘ | 0 |
#Constraintsⓘ | 46 |
#Linear Constraintsⓘ | 38 |
#Quadratic Constraintsⓘ | 0 |
#Polynomial Constraintsⓘ | 0 |
#Signomial Constraintsⓘ | 0 |
#General Nonlinear Constraintsⓘ | 8 |
Operands in Gen. Nonlin. Functionsⓘ | mul signpower vcpower |
Constraints curvatureⓘ | indefinite |
#Nonzeros in Jacobianⓘ | 311 |
#Nonlinear Nonzeros in Jacobianⓘ | 32 |
#Nonzeros in (Upper-Left) Hessian of Lagrangianⓘ | 48 |
#Nonzeros in Diagonal of Hessian of Lagrangianⓘ | 16 |
#Blocks in Hessian of Lagrangianⓘ | 9 |
Minimal blocksize in Hessian of Lagrangianⓘ | 1 |
Maximal blocksize in Hessian of Lagrangianⓘ | 15 |
Average blocksize in Hessian of Lagrangianⓘ | 2.555556 |
#Semicontinuitiesⓘ | 0 |
#Nonlinear Semicontinuitiesⓘ | 0 |
#SOS type 1ⓘ | 0 |
#SOS type 2ⓘ | 0 |
Minimal coefficientⓘ | 5.0671e-04 |
Maximal coefficientⓘ | 5.5000e+05 |
Infeasibility of initial pointⓘ | 1 |
Sparsity Jacobianⓘ | |
Sparsity Hessian of Lagrangianⓘ |
$offlisting * * Equation counts * Total E G L N X C B * 47 31 8 8 0 0 0 0 * * Variable counts * x b i s1s s2s sc si * Total cont binary integer sos1 sos2 scont sint * 136 24 112 0 0 0 0 0 * FX 1 * * Nonzero counts * Total const NL DLL * 424 392 32 0 * * Solve m using MINLP minimizing objvar; Variables x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19 ,x20,x21,x22,x23,b24,b25,b26,b27,b28,b29,b30,b31,b32,b33,b34,b35,b36 ,b37,b38,b39,b40,b41,b42,b43,b44,b45,b46,b47,b48,b49,b50,b51,b52,b53 ,b54,b55,b56,b57,b58,b59,b60,b61,b62,b63,b64,b65,b66,b67,b68,b69,b70 ,b71,b72,b73,b74,b75,b76,b77,b78,b79,b80,b81,b82,b83,b84,b85,b86,b87 ,b88,b89,b90,b91,b92,b93,b94,b95,b96,b97,b98,b99,b100,b101,b102,b103 ,b104,b105,b106,b107,b108,b109,b110,b111,b112,b113,b114,b115,b116 ,b117,b118,b119,b120,b121,b122,b123,b124,b125,b126,b127,b128,b129 ,b130,b131,b132,b133,b134,b135,objvar; Binary Variables b24,b25,b26,b27,b28,b29,b30,b31,b32,b33,b34,b35,b36,b37,b38 ,b39,b40,b41,b42,b43,b44,b45,b46,b47,b48,b49,b50,b51,b52,b53,b54,b55 ,b56,b57,b58,b59,b60,b61,b62,b63,b64,b65,b66,b67,b68,b69,b70,b71,b72 ,b73,b74,b75,b76,b77,b78,b79,b80,b81,b82,b83,b84,b85,b86,b87,b88,b89 ,b90,b91,b92,b93,b94,b95,b96,b97,b98,b99,b100,b101,b102,b103,b104 ,b105,b106,b107,b108,b109,b110,b111,b112,b113,b114,b115,b116,b117 ,b118,b119,b120,b121,b122,b123,b124,b125,b126,b127,b128,b129,b130 ,b131,b132,b133,b134,b135; Equations e1,e2,e3,e4,e5,e6,e7,e8,e9,e10,e11,e12,e13,e14,e15,e16,e17,e18,e19 ,e20,e21,e22,e23,e24,e25,e26,e27,e28,e29,e30,e31,e32,e33,e34,e35,e36 ,e37,e38,e39,e40,e41,e42,e43,e44,e45,e46,e47; e1.. - 2000*b24 - 5000*b25 - 8000*b26 - 11000*b27 - 16000*b28 - 23000*b29 - 32000*b30 - 50000*b31 - 60000*b32 - 90000*b33 - 130000*b34 - 170000*b35 - 300000*b36 - 550000*b37 - 2000*b38 - 5000*b39 - 8000*b40 - 11000*b41 - 16000*b42 - 23000*b43 - 32000*b44 - 50000*b45 - 60000*b46 - 90000*b47 - 130000*b48 - 170000*b49 - 300000*b50 - 550000*b51 - 2000*b52 - 5000*b53 - 8000*b54 - 11000*b55 - 16000*b56 - 23000*b57 - 32000*b58 - 50000*b59 - 60000*b60 - 90000*b61 - 130000*b62 - 170000*b63 - 300000*b64 - 550000*b65 - 2000*b66 - 5000*b67 - 8000*b68 - 11000*b69 - 16000*b70 - 23000*b71 - 32000*b72 - 50000*b73 - 60000*b74 - 90000*b75 - 130000*b76 - 170000*b77 - 300000*b78 - 550000*b79 - 2000*b80 - 5000*b81 - 8000*b82 - 11000*b83 - 16000*b84 - 23000*b85 - 32000*b86 - 50000*b87 - 60000*b88 - 90000*b89 - 130000*b90 - 170000*b91 - 300000*b92 - 550000*b93 - 2000*b94 - 5000*b95 - 8000*b96 - 11000*b97 - 16000*b98 - 23000*b99 - 32000*b100 - 50000*b101 - 60000*b102 - 90000*b103 - 130000*b104 - 170000*b105 - 300000*b106 - 550000*b107 - 2000*b108 - 5000*b109 - 8000*b110 - 11000*b111 - 16000*b112 - 23000*b113 - 32000*b114 - 50000*b115 - 60000*b116 - 90000*b117 - 130000*b118 - 170000*b119 - 300000*b120 - 550000*b121 - 2000*b122 - 5000*b123 - 8000*b124 - 11000*b125 - 16000*b126 - 23000*b127 - 32000*b128 - 50000*b129 - 60000*b130 - 90000*b131 - 130000*b132 - 170000*b133 - 300000*b134 - 550000*b135 + objvar =E= 0; e2.. - x8 + x9 + x10 =E= -0.02777; e3.. - x9 + x11 =E= -0.02777; e4.. - x10 + x12 + x13 =E= -0.03333; e5.. - x11 - x12 + x14 =E= -0.075; e6.. - x13 + x15 =E= -0.09167; e7.. - x14 - x15 =E= -0.05555; e8.. SignPower(x8,1.852) - 0.76849192909955*(1.27323954473516*x16)**2.435*(x1 - x2) =E= 0; e9.. SignPower(x9,1.852) - 0.76849192909955*(1.27323954473516*x17)**2.435*(x2 - x3) =E= 0; e10.. SignPower(x10,1.852) - 0.76849192909955*(1.27323954473516*x18)**2.435*(x2 - x4) =E= 0; e11.. SignPower(x11,1.852) - 0.76849192909955*(1.27323954473516*x19)**2.435*(x3 - x5) =E= 0; e12.. SignPower(x12,1.852) - 0.76849192909955*(1.27323954473516*x20)**2.435*(x4 - x5) =E= 0; e13.. SignPower(x13,1.852) - 0.76849192909955*(1.27323954473516*x21)**2.435*(x4 - x6) =E= 0; e14.. SignPower(x14,1.852) - 0.76849192909955*(1.27323954473516*x22)**2.435*(x5 - x7) =E= 0; e15.. SignPower(x15,1.852) - 0.76849192909955*(1.27323954473516*x23)**2.435*(x6 - x7) =E= 0; e16.. x8 - 2*x16 =L= 0; e17.. x9 - 2*x17 =L= 0; e18.. x10 - 2*x18 =L= 0; e19.. x11 - 2*x19 =L= 0; e20.. x12 - 2*x20 =L= 0; e21.. x13 - 2*x21 =L= 0; e22.. x14 - 2*x22 =L= 0; e23.. x15 - 2*x23 =L= 0; e24.. x8 + 2*x16 =G= 0; e25.. x9 + 2*x17 =G= 0; e26.. x10 + 2*x18 =G= 0; e27.. x11 + 2*x19 =G= 0; e28.. x12 + 2*x20 =G= 0; e29.. x13 + 2*x21 =G= 0; e30.. x14 + 2*x22 =G= 0; e31.. x15 + 2*x23 =G= 0; e32.. x16 - 0.000506707479097498*b24 - 0.00202682991638999*b25 - 0.00456036731187748*b26 - 0.00810731966555996*b27 - 0.0182414692475099*b28 - 0.0324292786622399*b29 - 0.0506707479097498*b30 - 0.0729658769900397*b31 - 0.0993146659031096*b32 - 0.129717114648959*b33 - 0.164173223227589*b34 - 0.202682991638999*b35 - 0.245246419883189*b36 - 0.291863507960159*b37 =E= 0; e33.. x17 - 0.000506707479097498*b38 - 0.00202682991638999*b39 - 0.00456036731187748*b40 - 0.00810731966555996*b41 - 0.0182414692475099*b42 - 0.0324292786622399*b43 - 0.0506707479097498*b44 - 0.0729658769900397*b45 - 0.0993146659031096*b46 - 0.129717114648959*b47 - 0.164173223227589*b48 - 0.202682991638999*b49 - 0.245246419883189*b50 - 0.291863507960159*b51 =E= 0; e34.. x18 - 0.000506707479097498*b52 - 0.00202682991638999*b53 - 0.00456036731187748*b54 - 0.00810731966555996*b55 - 0.0182414692475099*b56 - 0.0324292786622399*b57 - 0.0506707479097498*b58 - 0.0729658769900397*b59 - 0.0993146659031096*b60 - 0.129717114648959*b61 - 0.164173223227589*b62 - 0.202682991638999*b63 - 0.245246419883189*b64 - 0.291863507960159*b65 =E= 0; e35.. x19 - 0.000506707479097498*b66 - 0.00202682991638999*b67 - 0.00456036731187748*b68 - 0.00810731966555996*b69 - 0.0182414692475099*b70 - 0.0324292786622399*b71 - 0.0506707479097498*b72 - 0.0729658769900397*b73 - 0.0993146659031096*b74 - 0.129717114648959*b75 - 0.164173223227589*b76 - 0.202682991638999*b77 - 0.245246419883189*b78 - 0.291863507960159*b79 =E= 0; e36.. x20 - 0.000506707479097498*b80 - 0.00202682991638999*b81 - 0.00456036731187748*b82 - 0.00810731966555996*b83 - 0.0182414692475099*b84 - 0.0324292786622399*b85 - 0.0506707479097498*b86 - 0.0729658769900397*b87 - 0.0993146659031096*b88 - 0.129717114648959*b89 - 0.164173223227589*b90 - 0.202682991638999*b91 - 0.245246419883189*b92 - 0.291863507960159*b93 =E= 0; e37.. x21 - 0.000506707479097498*b94 - 0.00202682991638999*b95 - 0.00456036731187748*b96 - 0.00810731966555996*b97 - 0.0182414692475099*b98 - 0.0324292786622399*b99 - 0.0506707479097498*b100 - 0.0729658769900397*b101 - 0.0993146659031096*b102 - 0.129717114648959*b103 - 0.164173223227589*b104 - 0.202682991638999*b105 - 0.245246419883189*b106 - 0.291863507960159*b107 =E= 0; e38.. x22 - 0.000506707479097498*b108 - 0.00202682991638999*b109 - 0.00456036731187748*b110 - 0.00810731966555996*b111 - 0.0182414692475099*b112 - 0.0324292786622399*b113 - 0.0506707479097498*b114 - 0.0729658769900397*b115 - 0.0993146659031096*b116 - 0.129717114648959*b117 - 0.164173223227589*b118 - 0.202682991638999*b119 - 0.245246419883189*b120 - 0.291863507960159*b121 =E= 0; e39.. x23 - 0.000506707479097498*b122 - 0.00202682991638999*b123 - 0.00456036731187748*b124 - 0.00810731966555996*b125 - 0.0182414692475099*b126 - 0.0324292786622399*b127 - 0.0506707479097498*b128 - 0.0729658769900397*b129 - 0.0993146659031096*b130 - 0.129717114648959*b131 - 0.164173223227589*b132 - 0.202682991638999*b133 - 0.245246419883189*b134 - 0.291863507960159*b135 =E= 0; e40.. b24 + b25 + b26 + b27 + b28 + b29 + b30 + b31 + b32 + b33 + b34 + b35 + b36 + b37 =E= 1; e41.. b38 + b39 + b40 + b41 + b42 + b43 + b44 + b45 + b46 + b47 + b48 + b49 + b50 + b51 =E= 1; e42.. b52 + b53 + b54 + b55 + b56 + b57 + b58 + b59 + b60 + b61 + b62 + b63 + b64 + b65 =E= 1; e43.. b66 + b67 + b68 + b69 + b70 + b71 + b72 + b73 + b74 + b75 + b76 + b77 + b78 + b79 =E= 1; e44.. b80 + b81 + b82 + b83 + b84 + b85 + b86 + b87 + b88 + b89 + b90 + b91 + b92 + b93 =E= 1; e45.. b94 + b95 + b96 + b97 + b98 + b99 + b100 + b101 + b102 + b103 + b104 + b105 + b106 + b107 =E= 1; e46.. b108 + b109 + b110 + b111 + b112 + b113 + b114 + b115 + b116 + b117 + b118 + b119 + b120 + b121 =E= 1; e47.. b122 + b123 + b124 + b125 + b126 + b127 + b128 + b129 + b130 + b131 + b132 + b133 + b134 + b135 =E= 1; * set non-default bounds x1.fx = 210; x2.lo = 180; x2.up = 210; x3.lo = 190; x3.up = 210; x4.lo = 185; x4.up = 210; x5.lo = 180; x5.up = 210; x6.lo = 195; x6.up = 210; x7.lo = 190; x7.up = 210; x16.lo = 0.000506707479097498; x16.up = 0.291863507960159; x17.lo = 0.000506707479097498; x17.up = 0.291863507960159; x18.lo = 0.000506707479097498; x18.up = 0.291863507960159; x19.lo = 0.000506707479097498; x19.up = 0.291863507960159; x20.lo = 0.000506707479097498; x20.up = 0.291863507960159; x21.lo = 0.000506707479097498; x21.up = 0.291863507960159; x22.lo = 0.000506707479097498; x22.up = 0.291863507960159; x23.lo = 0.000506707479097498; x23.up = 0.291863507960159; Model m / all /; m.limrow=0; m.limcol=0; m.tolproj=0.0; $if NOT '%gams.u1%' == '' $include '%gams.u1%' $if not set MINLP $set MINLP MINLP Solve m using %MINLP% minimizing objvar;
Last updated: 2024-08-26 Git hash: 6cc1607f