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inspect the mip problem formulation

Source: R/inspect_mip.R
inspect_mip.Rd

Utility function to inspect the mip problem for a record. inspect_mip can be used as a "drop-in" replacement for locate_errors(), but works on the first record.

Usage

inspect_mip(data, x, weight, ...)

Arguments

data

data to be checked

x

validation rules or errorlocalizer object to be used for finding possible errors.

weight

numeric optional weight specification to be used in the error localization (see expand_weights()).

...

optional parameters that are passed to lpSolveAPI::lp.control() (see details)

Details

It may sometimes be handy to find out what is happening exactly with a record. See the example section for finding out what to do with inspect_mip. See vignette("inspect_mip") for more details.

See also

Other Mixed Integer Problem: MipRules-class

Examples

rules <- validator(x > 1)
data <- list(x = 0)
weight <- c(x = 1)

mip <- inspect_mip(data, rules)
print(mip)
#> Mip rules object:
#>    methods: '$to_lp()', '$execute', '$set_values()'
#>    properties: '$mip_rules', '$objective', '$is_infeasible', '$rules'
#> 
#> Generates the lp program (see ?inspect_mip) 
#> 
#> Model name: errorlocate
#>                        x        .delta_x            
#> Minimize               0  1.289767244598            
#> V1                    -1               0  <=  -1.001
#> x_ub                   1          -1e+07  <=       0
#> x_lb                  -1          -1e+07  <=       0
#> Kind                 Std             Std            
#> Type                Real             Int            
#> Upper                Inf               1            
#> Lower               -Inf               0            

# inspect the lp problem (prior to solving it with lpsolveAPI)
lp <- mip$to_lp()
print(lp)
#> Model name: errorlocate
#>                        x        .delta_x            
#> Minimize               0  1.289767244598            
#> V1                    -1               0  <=  -1.001
#> x_ub                   1          -1e+07  <=       0
#> x_lb                  -1          -1e+07  <=       0
#> Kind                 Std             Std            
#> Type                Real             Int            
#> Upper                Inf               1            
#> Lower               -Inf               0            

# for large problems write the lp problem to disk for inspection
# lpSolveAPI::write.lp(lp, "my_problem.lp")

# solve the mip system / find a solution
res <- mip$execute()
names(res)
#> [1] "s"        "solution" "values"   "lp"       "adapt"    "errors"  

# lpSolveAPI status of finding a solution
res$s
#> [1] 0

# lp problem after solving (often simplified version of first lp)
res$lp
#> Model name: errorlocate
#>                        x        .delta_x       
#> Minimize               0  1.289767244598       
#> x_ub                   1          -1e+07  <=  0
#> Kind                 Std             Std       
#> Type                Real             Int       
#> Upper                Inf               1       
#> Lower              1.001               0       

# records that are deemed "faulty"
res$errors
#>    x 
#> TRUE 

# values of variables used in the mip formulation. Also contains a valid solution
# for "faulty" variables
res$values
#>        x .delta_x 
#>    1.001    1.000 

# see the derived mip rules and objective function, used in the construction of
# lp problem
mip$mip_rules()
#> [[1]]
#> V1: -x < -1
#> [[2]]
#> x_ub: x - 1e+07*.delta_x <= 0
#> [[3]]
#> x_lb: -x - 1e+07*.delta_x <= 0
mip$objective
#> .delta_x 
#> 1.289767