如何在Julia+JuMP中定义类AMPL集和参数？_Julia_Julia Jump

如何在Julia+JuMP中定义类AMPL集和参数？

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如何在Julia+JuMP中定义类AMPL集和参数？,julia,julia-jump,Julia,Julia Jump,我需要在Julia+JuMP中定义一些常量参数，类似于在AMPL中定义 set A := a0 a1 a2; param p := a0 1 a1 5 a2 10 ; 如何在Julia中定义A和p之类的东西？除了Julia中可用的语法外，JuMP本身并没有为索引集定义特殊的语法。例如，您可以定义 A = [:a0, :a1, :a2] 其中：a0定义了一个符号如果要在此集合上为变量编制索引，则语法为： m = Model() @variable(m, x[A]) 跳转也不像AMPL那样

我需要在Julia+JuMP中定义一些常量参数，类似于在AMPL中定义

set A := a0 a1 a2;

param p :=
a0 1
a1 5
a2 10 ;

如何在Julia中定义A和p之类的东西？

除了Julia中可用的语法外，JuMP本身并没有为索引集定义特殊的语法。例如，您可以定义

A = [:a0, :a1, :a2]

其中：a0定义了一个符号

如果要在此集合上为变量编制索引，则语法为：

m = Model()
@variable(m, x[A])

跳转也不像AMPL那样区分数据和模型，因此没有真正的参数概念。相反，您只需在使用数据时提供数据。如果我正确理解你的问题，你可以这样做

p = Dict(:a0 => 1, :a1 => 5, :a2 => 10)
@constraint(m, sum(p[i]*x[i] for i in A) <= 20)

其中foo是一个任意的Julia函数，可以执行数据库查找、计算pi的位数等。

跳转本身没有为索引集定义特殊语法，超出了Julia中可用的语法。例如，您可以定义

A = [:a0, :a1, :a2]

其中：a0定义了一个符号

如果要在此集合上为变量编制索引，则语法为：

m = Model()
@variable(m, x[A])

跳转也不像AMPL那样区分数据和模型，因此没有真正的参数概念。相反，您只需在使用数据时提供数据。如果我正确理解你的问题，你可以这样做

p = Dict(:a0 => 1, :a1 => 5, :a2 => 10)
@constraint(m, sum(p[i]*x[i] for i in A) <= 20)

其中foo是一个任意的Julia函数，可以执行数据库查找、计算pi的位数等。

我无法得到@mlubin工作的原始答案。此外，网络上的许多示例都使用基于位置的索引，这让我感觉不太自然，因此我用字典改写了GAMS教程的示例。。感觉更接近gams/ampl集合

#=
Transposition in JuMP of the basic transport model used in the GAMS tutorial

This problem finds a least cost shipping schedule that meets requirements at markets and supplies at factories.

- Original formulation: Dantzig, G B, Chapter 3.3. In Linear Programming and Extensions.
Princeton University Press, Princeton, New Jersey, 1963.
- Gams implementation: This formulation is described in        detail in:
Rosenthal, R E, Chapter 2: A GAMS Tutorial. In GAMS: A User's Guide.
The Scientific Press, Redwood City, California, 1988.
- JuMP implementation: Antonello Lobianco
=#

using JuMP, DataFrames

# Sets
plants  = ["seattle","san_diego"]          # canning plants
markets = ["new_york","chicago","topeka"]  # markets

# Parameters
a = Dict(              # capacity of plant i in cases
  "seattle"   => 350,
  "san_diego" => 600,
)
b = Dict(              # demand at market j in cases
  "new_york"  => 325,
  "chicago"   => 300,
  "topeka"    => 275,
)

#  distance in thousands of miles
d_table = wsv"""
plants     new_york  chicago  topeka
seattle    2.5       1.7      1.8
san_diego  2.5       1.8      1.4
"""
d = Dict( (r[:plants],m) => r[Symbol(m)] for r in       eachrow(d_table), m in markets)

f = 90 # freight in dollars per case per thousand miles

c = Dict() # transport cost in thousands of dollars per case ;
[ c[p,m] = f * d[p,m] / 1000 for p in plants, m in markets]

# Model declaration
trmodel = Model() # transport model

# Variables
@variables trmodel begin
    x[p in plants, m in markets] >= 0 # shipment quantities in cases
end

# Constraints
@constraints trmodel begin
    supply[p in plants],   # observe supply limit at plant p
        sum(x[p,m] for m in markets)  <=  a[p]
    demand[m in markets],  # satisfy demand at market m
        sum(x[p,m] for p in plants)  >=  b[m]
 end

# Objective
@objective trmodel Min begin
    sum(c[p,m]*x[p,m] for p in plants, m in markets)
end

print(trmodel)

status = solve(trmodel)

if status == :Optimal
    println("Objective value: ", getobjectivevalue(trmodel))
    println("Shipped quantities: ")
    println(getvalue(x))
    println("Shadow prices of supply:")
    [println("$p = $(getdual(supply[p]))") for p in plants]
    println("Shadow prices of demand:")
    [println("$m = $(getdual(demand[m]))") for m in markets]

else
    println("Model didn't solved")
    println(status)
end

# Expected result:
# obj= 153.675
#['seattle','new-york']   = 50
#['seattle','chicago']    = 300
#['seattle','topeka']     = 0
#['san-diego','new-york'] = 275
#['san-diego','chicago']  = 0
#['san-diego','topeka']   = 275

我的网站上有一个评论更多的版本。

#=
Transposition in JuMP of the basic transport model used in the GAMS tutorial

This problem finds a least cost shipping schedule that meets requirements at markets and supplies at factories.

- Original formulation: Dantzig, G B, Chapter 3.3. In Linear Programming and Extensions.
Princeton University Press, Princeton, New Jersey, 1963.
- Gams implementation: This formulation is described in        detail in:
Rosenthal, R E, Chapter 2: A GAMS Tutorial. In GAMS: A User's Guide.
The Scientific Press, Redwood City, California, 1988.
- JuMP implementation: Antonello Lobianco
=#

using JuMP, DataFrames

# Sets
plants  = ["seattle","san_diego"]          # canning plants
markets = ["new_york","chicago","topeka"]  # markets

# Parameters
a = Dict(              # capacity of plant i in cases
  "seattle"   => 350,
  "san_diego" => 600,
)
b = Dict(              # demand at market j in cases
  "new_york"  => 325,
  "chicago"   => 300,
  "topeka"    => 275,
)

#  distance in thousands of miles
d_table = wsv"""
plants     new_york  chicago  topeka
seattle    2.5       1.7      1.8
san_diego  2.5       1.8      1.4
"""
d = Dict( (r[:plants],m) => r[Symbol(m)] for r in       eachrow(d_table), m in markets)

f = 90 # freight in dollars per case per thousand miles

c = Dict() # transport cost in thousands of dollars per case ;
[ c[p,m] = f * d[p,m] / 1000 for p in plants, m in markets]

# Model declaration
trmodel = Model() # transport model

# Variables
@variables trmodel begin
    x[p in plants, m in markets] >= 0 # shipment quantities in cases
end

# Constraints
@constraints trmodel begin
    supply[p in plants],   # observe supply limit at plant p
        sum(x[p,m] for m in markets)  <=  a[p]
    demand[m in markets],  # satisfy demand at market m
        sum(x[p,m] for p in plants)  >=  b[m]
 end

# Objective
@objective trmodel Min begin
    sum(c[p,m]*x[p,m] for p in plants, m in markets)
end

print(trmodel)

status = solve(trmodel)

if status == :Optimal
    println("Objective value: ", getobjectivevalue(trmodel))
    println("Shipped quantities: ")
    println(getvalue(x))
    println("Shadow prices of supply:")
    [println("$p = $(getdual(supply[p]))") for p in plants]
    println("Shadow prices of demand:")
    [println("$m = $(getdual(demand[m]))") for m in markets]

else
    println("Model didn't solved")
    println(status)
end

# Expected result:
# obj= 153.675
#['seattle','new-york']   = 50
#['seattle','chicago']    = 300
#['seattle','topeka']     = 0
#['san-diego','new-york'] = 275
#['san-diego','chicago']  = 0
#['san-diego','topeka']   = 275

my上提供了更多评论版本。

此答案非常有用。无论如何，我认为数据不应该在代码中。我看到两种解决方案：1如果可能，从文件中读取常量。2通过脚本生成代码，该脚本填充从xml读取的数据。从文件中读取数据当然是合理的。JuMP的理念是让用户决定如何构造输入，而不是强加某些文件格式。例如，在上面，您可以使用Julia的本机I/O函数或软件包从文件中填充p。非常感谢，我尝试搜索有关它的内容。@mlubin您如何继续定义目标？我在A中尝试了@objectivem，Max，sumx[I]+p[I]，但没有成功。实际上，Julia 0.5在编写CONSTRAINT语句时给了我一个错误：LoadError:不支持类型为元组{Symbol}的索引数组{Pair{Symbol，Int64}，1}。我已经更正了p定义中的一个错误。代码现在应该可以工作了。这个答案非常有用。无论如何，我认为数据不应该在代码中。我看到两种解决方案：1如果可能，从文件中读取常量。2通过脚本生成代码，该脚本填充从xml读取的数据。从文件中读取数据当然是合理的。JuMP的理念是让用户决定如何构造输入，而不是强加某些文件格式。例如，在上面，您可以使用Julia的本机I/O函数或软件包从文件中填充p。非常感谢，我尝试搜索有关它的内容。@mlubin您如何继续定义目标？我在A中尝试了@objectivem，Max，sumx[I]+p[I]，但没有成功。实际上，Julia 0.5在编写CONSTRAINT语句时给了我一个错误：LoadError:不支持类型为元组{Symbol}的索引数组{Pair{Symbol，Int64}，1}。我已经更正了p定义中的一个错误。代码现在应该可以工作了。