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Linear Programming

I wonder if there is not a little display bug inside the Sagemath linear program since the display of the result here is given before the display of the program (for some other programs it is given inside).

nmd= 10 #nombre de contraintes
mmd= 6 #nombre de variables
Amd= matrix(nmd,mmd,[250,770,360,190,230,-1,0,0,0,0,0,1,31,44,14,27,3,0,480,1770,800,580,160,0, 1,0,0,0,0,0, 0,1,0,0,0,0, 0,0,1,0,0,0, 0,0,0,1,0,0, 0,0,0,0,1,0, 0,0,0,0,0,1]) #les coefficients pour chaque contraintes
bmdmin=[0,600,30,0,0,0,0,0,0] #les bornes inférieures pour les contraintes
bmdmax=[0,900,1000,Infinity,Infinity,Infinity,Infinity,Infinity,Infinity,Infinity] #les bornes supérieures pour les  contraintes (oo=infini)
show(LatexExpr("A="),A1)
show(LatexExpr("bmin="),bmdmin)
show(LatexExpr("bmax="),bmdmax)
md=MixedIntegerLinearProgram(maximization=False, solver="GLPK",) #on crée le programme de maximization
x=md.new_variable(integer=False,nonnegative=True, indices=[0..mmd-1]) #les nouvelles variables sont x_0...x_2 #integer=nombre entier
Bmd=Amd*x #la fonction linéaire pour les contraintes
md.set_objective(1.2*x[0]+4.5*x[1]+3.7*x[2]+6.3*x[3]+3.2*x[4]) # fixe l'objectif
#on construit les contraintes avec leurs bornes
for i in range(0,4):
    md.add_constraint(Bmd[i], min=bmdmin[i], max=bmdmax[i])
md.show()
md.solve()
md.get_values(x)
xmd=md.get_values(x)
show(xmd)

Of course I am aware that without the show() command there is no problem. But, for uniformity of presentation I need this command. As I could call it in an other cell the problem an be circonvened. But for economic reasons I would prefer to have the code and the result in the same cell. Nevertheless, this is just a remark.

Linear Programming

I wonder if there is not a little display bug inside the Sagemath SageMath linear program since the display of the result here is given given before the display of the program (for some other programs programs it is given inside).

nmd= 10 #nombre # Nombre de contraintes
mmd= 6 #nombre contraintes et de variables
Amd= matrix(nmd,mmd,[250,770,360,190,230,-1,0,0,0,0,0,1,31,44,14,27,3,0,480,1770,800,580,160,0, 1,0,0,0,0,0, 0,1,0,0,0,0, 0,0,1,0,0,0, 0,0,0,1,0,0, 0,0,0,0,1,0, 0,0,0,0,0,1]) #les coefficients variables.
nmd, mmd = 10, 6
# Coefficients pour chaque contraintes
bmdmin=[0,600,30,0,0,0,0,0,0] #les bornes contrainte.
coeffs = [(250, 770, 360, 190, 230, -1),
          (0, 0, 0, 0, 0, 1),
          (31, 44, 14, 27, 3, 0),
          (480, 1770, 800, 580, 160, 0),
          (1, 0, 0, 0, 0, 0),
          (0, 1, 0, 0, 0, 0),
          (0, 0, 1, 0, 0, 0),
          (0, 0, 0, 1, 0, 0),
          (0, 0, 0, 0, 1, 0),
          (0, 0, 0, 0, 0, 1)]
# Matrice des contraintes.
Amd = matrix(nmd, mmd, coeffs)
# Bornes inférieures pour les contraintes
bmdmax=[0,900,1000,Infinity,Infinity,Infinity,Infinity,Infinity,Infinity,Infinity] #les bornes contraintes.
bmdmin = [0, 600, 30, 0, 0, 0, 0, 0, 0]
# Bornes supérieures pour les  contraintes (oo=infini)
show(LatexExpr("A="),A1)
show(LatexExpr("bmin="),bmdmin)
show(LatexExpr("bmax="),bmdmax)
md=MixedIntegerLinearProgram(maximization=False, solver="GLPK",) #on (oo = infini).
bmdmax = [0, 900, 1000, oo, oo, oo, oo, oo, oo, oo]
# Visualisons.
show(LatexExpr("A="), Amd)
show(LatexExpr("bmin="), bmdmin)
show(LatexExpr("bmax="), bmdmax)
# On crée le programme de maximization
x=md.new_variable(integer=False,nonnegative=True, indices=[0..mmd-1]) #les nouvelles maximisation.
md = MixedIntegerLinearProgram(maximization=False, solver="GLPK")
# Nouvelles variables: x_0 ... x_2; 'integer' dit si variables sont x_0...x_2 #integer=nombre entier
Bmd=Amd*x #la fonction à valeurs entières
x = md.new_variable(integer=False, nonnegative=True, indices=[0 .. mmd - 1])
# Fonction linéaire pour les contraintes
md.set_objective(1.2*x[0]+4.5*x[1]+3.7*x[2]+6.3*x[3]+3.2*x[4]) # contraintes.
Bmd = Amd * x
# On fixe l'objectif
#on l'objectif.
md.set_objective(1.2*x[0] + 4.5*x[1] + 3.7*x[2] + 6.3*x[3] + 3.2*x[4])
# On construit les contraintes avec leurs bornes
bornes.
for i in range(0,4):
    md.add_constraint(Bmd[i], min=bmdmin[i], max=bmdmax[i])
md.show()
md.solve()
md.get_values(x)
xmd=md.get_values(x)
xmd = md.get_values(x)
# Le résultat.
show(xmd)

Output:

𝐴=(...)

𝑏𝑚𝑖𝑛=[0,600,30,0,0,0,0,0,0]

𝑏𝑚𝑎𝑥=[0,900,1000,+∞,+∞,+∞,+∞,+∞,+∞,+∞]

Minimization:
  1.2 x_0 + 4.5 x_1 + 3.7 x_2 + 6.3 x_3 + 3.2 x_4 

Constraints:
  0.0 <= 250.0 x_0 + 770.0 x_1 + 360.0 x_2 + 190.0 x_3 + 230.0 x_4 - x_5 <= 0.0
  600.0 <= x_5 <= 900.0
  30.0 <= 31.0 x_0 + 44.0 x_1 + 14.0 x_2 + 27.0 x_3 + 3.0 x_4 <= 1000.0
  0.0 <= 480.0 x_0 + 1770.0 x_1 + 800.0 x_2 + 580.0 x_3 + 160.0 x_4 <= inf
Variables:
  x_0 is a continuous variable (min=0.0, max=+oo)
  x_1 is a continuous variable (min=0.0, max=+oo)
  x_2 is a continuous variable (min=0.0, max=+oo)
  x_3 is a continuous variable (min=0.0, max=+oo)
  x_4 is a continuous variable (min=0.0, max=+oo)
  x_5 is a continuous variable (min=0.0, max=+oo)

{0:2.4000000000000004,1:0.0,2:0.0,3:0.0,4:0.0,5:600.0000000000001}

Of course I am aware that without the show() command there is no problem. But, for uniformity of presentation I need this command. As I could call it in an other cell the problem an be circonvened. circumvented. But for economic reasons I would prefer to have the code and the result in the same cell. Nevertheless, this is just a remark.

Linear Programming

I wonder if there is not a little display bug inside the SageMath linear program since the display of the result here is given before the display of the program (for some other programs it is given inside).

# Nombre de contraintes et de variables.
nmd, mmd = 10, 6
# Coefficients pour chaque contrainte.
coeffs = [(250, 770, 360, 190, 230, -1),
          (0, 0, 0, 0, 0, 1),
          (31, 44, 14, 27, 3, 0),
          (480, 1770, 800, 580, 160, 0),
          (1, 0, 0, 0, 0, 0),
          (0, 1, 0, 0, 0, 0),
          (0, 0, 1, 0, 0, 0),
          (0, 0, 0, 1, 0, 0),
          (0, 0, 0, 0, 1, 0),
          (0, 0, 0, 0, 0, 1)]
# Matrice des contraintes.
Amd = matrix(nmd, mmd, coeffs)
# Bornes inférieures pour les contraintes.
bmdmin = [0, 600, 30, 0, 0, 0, 0, 0, 0]
# Bornes supérieures pour les  contraintes (oo = infini).
bmdmax = [0, 900, 1000, oo, oo, oo, oo, oo, oo, oo]
# Visualisons.
show(LatexExpr("A="), Amd)
show(LatexExpr("bmin="), bmdmin)
show(LatexExpr("bmax="), bmdmax)
# On crée le programme de maximisation.
md = MixedIntegerLinearProgram(maximization=False, solver="GLPK")
# Nouvelles variables: x_0 ... x_2; 'integer' dit si variables à valeurs entières
x = md.new_variable(integer=False, nonnegative=True, indices=[0 .. mmd - 1])
# Fonction linéaire pour les contraintes.
Bmd = Amd * x
# On fixe l'objectif.
md.set_objective(1.2*x[0] + 4.5*x[1] + 3.7*x[2] + 6.3*x[3] + 3.2*x[4])
# On construit les contraintes avec leurs bornes.
for i in range(0,4):
    md.add_constraint(Bmd[i], min=bmdmin[i], max=bmdmax[i])
md.show()
md.solve()
md.get_values(x)
xmd = md.get_values(x)
# Le résultat.
show(xmd)

Output:

𝐴=(...)

𝑏𝑚𝑖𝑛=[0,600,30,0,0,0,0,0,0]

𝑏𝑚𝑎𝑥=[0,900,1000,+∞,+∞,+∞,+∞,+∞,+∞,+∞]

Minimization:
  1.2 x_0 + 4.5 x_1 + 3.7 x_2 + 6.3 x_3 + 3.2 x_4 

Constraints:
  0.0 <= 250.0 x_0 + 770.0 x_1 + 360.0 x_2 + 190.0 x_3 + 230.0 x_4 - x_5 <= 0.0
  600.0 <= x_5 <= 900.0
  30.0 <= 31.0 x_0 + 44.0 x_1 + 14.0 x_2 + 27.0 x_3 + 3.0 x_4 <= 1000.0
  0.0 <= 480.0 x_0 + 1770.0 x_1 + 800.0 x_2 + 580.0 x_3 + 160.0 x_4 <= inf
Variables:
  x_0 is a continuous variable (min=0.0, max=+oo)
  x_1 is a continuous variable (min=0.0, max=+oo)
  x_2 is a continuous variable (min=0.0, max=+oo)
  x_3 is a continuous variable (min=0.0, max=+oo)
  x_4 is a continuous variable (min=0.0, max=+oo)
  x_5 is a continuous variable (min=0.0, max=+oo)

{0:2.4000000000000004,1:0.0,2:0.0,3:0.0,4:0.0,5:600.0000000000001}

Of course I am aware that without the show() command there is no problem. But, for uniformity of presentation I need this command. As I could call it in an other cell the problem an be circumvented. But for economic reasons I would prefer to have the code and the result in the same cell. Nevertheless, this is just a remark.

Answer to the remark of slelievre

Inside a notebook this works perfectly. But inside a Sagematjh Cell here is the result

image description

that is the result is printed before the program and some times inside it

Linear Programming

I wonder if there is not a little display bug inside the SageMath linear program since the display of the result here is given before the display of the program (for some other programs it is given inside).

# Nombre de contraintes et de variables.
nmd, mmd = 10, 6
# Coefficients pour chaque contrainte.
coeffs = [(250, 770, 360, 190, 230, -1),
          (0, 0, 0, 0, 0, 1),
          (31, 44, 14, 27, 3, 0),
          (480, 1770, 800, 580, 160, 0),
          (1, 0, 0, 0, 0, 0),
          (0, 1, 0, 0, 0, 0),
          (0, 0, 1, 0, 0, 0),
          (0, 0, 0, 1, 0, 0),
          (0, 0, 0, 0, 1, 0),
          (0, 0, 0, 0, 0, 1)]
# Matrice des contraintes.
Amd = matrix(nmd, mmd, coeffs)
# Bornes inférieures pour les contraintes.
bmdmin = [0, 600, 30, 0, 0, 0, 0, 0, 0]
# Bornes supérieures pour les  contraintes (oo = infini).
bmdmax = [0, 900, 1000, oo, oo, oo, oo, oo, oo, oo]
# Visualisons.
show(LatexExpr("A="), Amd)
show(LatexExpr("bmin="), bmdmin)
show(LatexExpr("bmax="), bmdmax)
# On crée le programme de maximisation.
md = MixedIntegerLinearProgram(maximization=False, solver="GLPK")
# Nouvelles variables: x_0 ... x_2; 'integer' dit si variables à valeurs entières
x = md.new_variable(integer=False, nonnegative=True, indices=[0 .. mmd - 1])
# Fonction linéaire pour les contraintes.
Bmd = Amd * x
# On fixe l'objectif.
md.set_objective(1.2*x[0] + 4.5*x[1] + 3.7*x[2] + 6.3*x[3] + 3.2*x[4])
# On construit les contraintes avec leurs bornes.
for i in range(0,4):
    md.add_constraint(Bmd[i], min=bmdmin[i], max=bmdmax[i])
md.show()
md.solve()
md.get_values(x)
xmd = md.get_values(x)
# Le résultat.
show(xmd)

Output:

𝐴=(...)
 𝑏𝑚𝑖𝑛=[0,600,30,0,0,0,0,0,0]
 𝑏𝑚𝑎𝑥=[0,900,1000,+∞,+∞,+∞,+∞,+∞,+∞,+∞]

Minimization:
  1.2 x_0 + 4.5 x_1 + 3.7 x_2 + 6.3 x_3 + 3.2 x_4 

Constraints:
  0.0 <= 250.0 x_0 + 770.0 x_1 + 360.0 x_2 + 190.0 x_3 + 230.0 x_4 - x_5 <= 0.0
  600.0 <= x_5 <= 900.0
  30.0 <= 31.0 x_0 + 44.0 x_1 + 14.0 x_2 + 27.0 x_3 + 3.0 x_4 <= 1000.0
  0.0 <= 480.0 x_0 + 1770.0 x_1 + 800.0 x_2 + 580.0 x_3 + 160.0 x_4 <= inf
Variables:
  x_0 is a continuous variable (min=0.0, max=+oo)
  x_1 is a continuous variable (min=0.0, max=+oo)
  x_2 is a continuous variable (min=0.0, max=+oo)
  x_3 is a continuous variable (min=0.0, max=+oo)
  x_4 is a continuous variable (min=0.0, max=+oo)
  x_5 is a continuous variable (min=0.0, max=+oo)

{0:2.4000000000000004,1:0.0,2:0.0,3:0.0,4:0.0,5:600.0000000000001}

Of course I am aware that without the show() command there is no problem. But, for uniformity of presentation I need this command. As I could call it in an other cell the problem an be circumvented. But for economic reasons I would prefer to have the code and the result in the same cell. Nevertheless, this is just a remark.

Answer Edit to the remark of slelievreaddress a comment by slelievre.

Inside a notebook this works perfectly. But inside a "Sage Cell" here is the result:

Sagematjh Cell𝐴=(...)
𝑏𝑚𝑖𝑛=[0,600,30,0,0,0,0,0,0]
𝑏𝑚𝑎𝑥=[0,900,1000,+∞,+∞,+∞,+∞,+∞,+∞,+∞]

{0:2.4000000000000004,1:0.0,2:0.0,3:0.0,4:0.0,5:600.0000000000001}

Minimization:
  1.2 x_0 + 4.5 x_1 + 3.7 x_2 + 6.3 x_3 + 3.2 x_4 

Constraints:
  0.0 <= 250.0 x_0 + 770.0 x_1 + 360.0 x_2 + 190.0 x_3 + 230.0 x_4 - x_5 <= 0.0
  600.0 <= x_5 <= 900.0
  30.0 <= 31.0 x_0 + 44.0 x_1 + 14.0 x_2 + 27.0 x_3 + 3.0 x_4 <= 1000.0
  0.0 <= 480.0 x_0 + 1770.0 x_1 + 800.0 x_2 + 580.0 x_3 + 160.0 x_4 <= inf
Variables:
  x_0 is a continuous variable (min=0.0, max=+oo)
  x_1 is a continuous variable (min=0.0, max=+oo)
  x_2 is a continuous variable (min=0.0, max=+oo)
  x_3 is a continuous variable (min=0.0, max=+oo)
  x_4 is a continuous variable (min=0.0, max=+oo)
  x_5 is a continuous variable (min=0.0, max=+oo)
 here is the result

image description

that is the result That is, the result, {0:2.40...,1:0.0,2:0.0,3:0.0,4:0.0,5:600.00...}, is printed before the program program's output, and some times sometimes inside itit.