Utilities for parsing MPS and SMPS file formats.
Project description
pysmps
This is a utility script for parsing MPS and SMPS file formats. It offers two main functions load_mps
for loading mps files and load_smps
for loading smps file directory.
load_mps
The load_mps(path)
method takes a path
variable as input. It should be a .cor or .mps file.
It opens the file with readpermissions and parses the described linear program into the following format:
name
: The name given to the linear program (can't be blank)objective_name
: The name of the objective function valuerow_names
: list of row namescol_names
: list of column namestypes
: list of constraint type indicators, i.e. either "E", "L" or "G" for equality, lower/equal or greater/equal constraint respectively.c
: the objective function coefficientsA
: the constraint matrixrhs_names
: list of names of right hand sides (there can be multiple right hand side components be defined, seldom more than one though)rhs
: dictionary(rhs_name) => b
, whereb
is the vector of constraint values for that given right hand side name.bnd_names
: list of names of boxbounds (seldom more than one)bnd
: dictionary(bnd_name) => {"LO": v_l, "UP": v_u}
wherev_l
is the vector of lower bounds andv_u
is the vector of upper bound values (defaults tov_l = 0
andv_u = +inf
).
Finally this corresponds to the linear program
min c * x s.t. for each rhs_name with corresponding b: A[types == "E",:] * x = b[types == "E"] A[types == "L",:] * x <= b[types == "L"] A[types == "G",:] * x >= b[types == "G"] for each bnd_name with corresponding v_l and v_u: v_l <= x < v_u
load_smps
This function makes use of the load_mps
function for parsing the .cor file. The SMPS file format consists of three files, a .cor, .tim and .sto file. The .cor file is in MPS format. Further the function expects a parameter path
to be such that path + ".cor"
is the core file, path + ".tim"
the time file and path + ".sto"
is the stochastic file.
It does not support scenarios yet!
It returns a stochastic multistage problem in the following format

name
: name of the program (must be the same in all 3 files) 
objective_name
: name of the objective function value 
constraints
: list of tuples(name, period, type)
for each constraint. It gives a name, a period in which the constraints appears and a type, i.e. "E", "L" or "G" as in MPS. 
variables
: list of tuples(name, period)
for each variable. It defines a name and a period in which the variable joins the program. 
c
: vector of objective function coefficients (of all periods) 
A
: matrix of constraint coefficients (of all periods) 
rhs_names
: list of rhs names as in MPS 
rhs
: dictionary as in MPS 
bounds
: dictionary as in MPS 
periods
: list of all periods appearing.len(periods)
is the number stages. 
blocks
: dictionary ofBlock
,LinearTransform
orSubRoutine
objects. Dependent on what the .sto file defined.Blocks
are independent random variables (every case of aBlock
must be combined with each case of anotherBlock
to get all possible appearences; the probabilities multiply),LinearTransform
are linear transformations of continuous random variables. The user needs to write the sample script on his own.SubRoutine
is a leftout in the file; it presupposes the user to know what to do with these values. 
independent_variables
: dictionary((i,j)) => {position, period, distrib}
, where(i,j)
is the tuple of row/column indices. If one of them is1
this means that it's either an objective value or a rhsvalue respectively.position
is a dictionary adapting to where the entry is (objective value, rhs value or matrix value),period
defines the period in which this variable is stochastic,distrib
is either a definition of a continuous random variablesdistrib: {type: "N(mu, sigma**2)"/"U(a, b)"/"B(p, q)"/"G(p, b)"/"LN(mu, sigma**2)", parameters}
where parameters is a dictionary defining the required parameters. In the discrete case it is a list of tuples
(v,p)
, wherev
is the value of this position andp
is the probability of it appearing.
For an example on how to use this format i recommand looking at the code for load_2stage_problem
.
load_2stage_problem
Loads a SMPS directory and tries to bring it into a 2staged stochastic linear program with fixed recourse. Output is a dictionary containing the values
c
: first stage objective function valueA
: first stage (equality) constraint coefficient matrixb
: first stage constraint valuesq
: list of second stage objective function coefficients (each case one entry)h
: list of second stage constraint values (each case one entry)T
: list of second stage constraint values for deterministic variables (each case one entry)W
: recourse matrix (since it's fixed recourse this is not a list)p
: list of probabilities for each case
The constellations in which (q,h,T,W)
appear are the realizations given by (q[k], h[k], T[k], W)
.
The problem then resembles one of the form
min c * x + E_p[q * y] s.t. A * x = b T * x + W * y = h x, y >= 0
which is a formal expression since T and h are also stochastic. In fact this notation means we assert the stochastic constraints inside of the expectation, making it a function of x only.
For casting the SMPS files into such a form we need to make certain assertments:
 The upper right matrix needs to be zeroes only.
 We only have one righthand side defined (
len(rhs_names) == 1
).  There are no boundaries or if we defined some they are the default values.
 The first period parsed from the time file is the deterministic one, the other one is the stochastic one (especially there can only be two periods).
A
andW
are not stochastic.
This script however does
 convert inequality constraints (deterministic and stochastic) into equality constraints by adding slack variables at the right places
 calculate all combinations of independent accurances of stochastic components (BLOCKS and INDEP)
 calculate the probabilities as products of independent elementary probabilities alongside.
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