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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 read-permissions 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 value
  • row_names: list of row names
  • col_names: list of column names
  • types: 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 coefficients
  • A: the constraint matrix
  • rhs_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, where b is the vector of constraint values for that given right hand side name.
  • bnd_names: list of names of box-bounds (seldom more than one)
  • bnd: dictionary (bnd_name) => {"LO": v_l, "UP": v_u} where v_l is the vector of lower bounds and v_u is the vector of upper bound values (defaults to v_l = 0 and v_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 multi-stage 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 of Block,LinearTransform or SubRoutine objects. Dependent on what the .sto file defined. Blocks are independent random variables (every case of a Block must be combined with each case of another Block 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 left-out 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 is -1 this means that it's either an objective value or a rhs-value 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 variables

    distrib: {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), where v is the value of this position and p 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 2-staged stochastic linear program with fixed recourse. Output is a dictionary containing the values

  • c: first stage objective function value
  • A: first stage (equality) constraint coefficient matrix
  • b: first stage constraint values
  • q: 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 and W 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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