Stochastic Programming¶
To express a stochastic program in PySP, the user specifies both the deterministic base model and the scenario tree model with associated uncertain parameters. Both concrete and abstract model representations are supported.
Given the deterministic and scenario tree models, PySP provides multiple paths for the solution of the corresponding stochastic program. One alternative involves forming the extensive form and invoking an appropriate deterministic solver for the entire problem once. For more complex stochastic programs, we provide a generic implementation of Rockafellar and Wets’ Progressive Hedging algorithm, with additional specializations for approximating mixed-integer stochastic programs as well as other decomposition methods. By leveraging the combination of a high-level programming language (Python) and the embedding of the base deterministic model in that language (Pyomo), we are able to provide completely generic and highly configurable solver implementations.
This section describes PySP: (Pyomo Stochastic Programming), where parameters are allowed to be uncertain.
Overview of Modeling Components and Processes¶
The sequence of activities is typically the following:
Create a deterministic model and declare components
Develop base-case data for the deterministic model
Test, verify and validate the deterministic model
Model the stochastic processes
Develop a way to generate scenarios (in the form of a tree if there are more than two stages)
Create the data files need to describe the stochastics
Use PySP to solve stochastic problem
When viewed from the standpoint of file creation, the process is
Create an abstract model for the deterministic problem in a file called
ReferenceModel.py
Specify the stochastics in a file called
ScenarioStructure.dat
Specify scenario data
Birge and Louveaux’s Farmer Problem¶
Birge and Louveaux [BirgeLouveauxBook] make use of the example of a farmer who has 500 acres that can be planted in wheat, corn or sugar beets, at a per acre cost of 150, 230 and 260 (Euros, presumably), respectively. The farmer needs to have at least 200 tons of wheat and 240 tons of corn to use as feed, but if enough is not grown, those crops can be purchased for 238 and 210, respectively. Corn and wheat grown in excess of the feed requirements can be sold for 170 and 150, respectively. A price of 36 per ton is guaranteed for the first 6000 tons grown by any farmer, but beets in excess of that are sold for 10 per ton. The yield is 2.5, 3, and 20 tons per acre for wheat, corn and sugar beets, respectively.
ReferenceModel.py¶
So far, this is a deterministic problem because we are assuming that we
know all the data. The Pyomo model for this problem shown here is in the
file ReferenceModel.py
in the sub-directory
examples/farmer/models
that is distributed with PySP.
# ___________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
# Under the terms of Contract DE-NA0003525 with National Technology and
# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
# Farmer: rent out version has a scalar root node var
# note: this will minimize
#
# Imports
#
from pyomo.core import *
#
# Model
#
model = AbstractModel()
#
# Parameters
#
model.CROPS = Set()
model.TOTAL_ACREAGE = Param(within=PositiveReals)
model.PriceQuota = Param(model.CROPS, within=PositiveReals)
model.SubQuotaSellingPrice = Param(model.CROPS, within=PositiveReals)
def super_quota_selling_price_validate (model, value, i):
return model.SubQuotaSellingPrice[i] >= model.SuperQuotaSellingPrice[i]
model.SuperQuotaSellingPrice = Param(model.CROPS, validate=super_quota_selling_price_validate)
model.CattleFeedRequirement = Param(model.CROPS, within=NonNegativeReals)
model.PurchasePrice = Param(model.CROPS, within=PositiveReals)
model.PlantingCostPerAcre = Param(model.CROPS, within=PositiveReals)
model.Yield = Param(model.CROPS, within=NonNegativeReals)
#
# Variables
#
model.DevotedAcreage = Var(model.CROPS, bounds=(0.0, model.TOTAL_ACREAGE))
model.QuantitySubQuotaSold = Var(model.CROPS, bounds=(0.0, None))
model.QuantitySuperQuotaSold = Var(model.CROPS, bounds=(0.0, None))
model.QuantityPurchased = Var(model.CROPS, bounds=(0.0, None))
#
# Constraints
#
def ConstrainTotalAcreage_rule(model):
return summation(model.DevotedAcreage) <= model.TOTAL_ACREAGE
model.ConstrainTotalAcreage = Constraint(rule=ConstrainTotalAcreage_rule)
def EnforceCattleFeedRequirement_rule(model, i):
return model.CattleFeedRequirement[i] <= (model.Yield[i] * model.DevotedAcreage[i]) + model.QuantityPurchased[i] - model.QuantitySubQuotaSold[i] - model.QuantitySuperQuotaSold[i]
model.EnforceCattleFeedRequirement = Constraint(model.CROPS, rule=EnforceCattleFeedRequirement_rule)
def LimitAmountSold_rule(model, i):
return model.QuantitySubQuotaSold[i] + model.QuantitySuperQuotaSold[i] - (model.Yield[i] * model.DevotedAcreage[i]) <= 0.0
model.LimitAmountSold = Constraint(model.CROPS, rule=LimitAmountSold_rule)
def EnforceQuotas_rule(model, i):
return (0.0, model.QuantitySubQuotaSold[i], model.PriceQuota[i])
model.EnforceQuotas = Constraint(model.CROPS, rule=EnforceQuotas_rule)
#
# Stage-specific cost computations
#
def ComputeFirstStageCost_rule(model):
return summation(model.PlantingCostPerAcre, model.DevotedAcreage)
model.FirstStageCost = Expression(rule=ComputeFirstStageCost_rule)
def ComputeSecondStageCost_rule(model):
expr = summation(model.PurchasePrice, model.QuantityPurchased)
expr -= summation(model.SubQuotaSellingPrice, model.QuantitySubQuotaSold)
expr -= summation(model.SuperQuotaSellingPrice, model.QuantitySuperQuotaSold)
return expr
model.SecondStageCost = Expression(rule=ComputeSecondStageCost_rule)
#
# PySP Auto-generated Objective
#
# minimize: sum of StageCosts
#
# An active scenario objective equivalent to that generated by PySP is
# included here for informational purposes.
def total_cost_rule(model):
return model.FirstStageCost + model.SecondStageCost
model.Total_Cost_Objective = Objective(rule=total_cost_rule, sense=minimize)
Example Data¶
The data introduced here are in the file AverageScenario.dat in the
sub-directory examples/farmer/scenariodata
that is distributed with
Pyomo. These data are given for illustration. The file
ReferenceModel.dat is not required by PySP.
# "mean" scenario
set CROPS := WHEAT CORN SUGAR_BEETS ;
param TOTAL_ACREAGE := 500 ;
# no quotas on wheat or corn
param PriceQuota := WHEAT 100000 CORN 100000 SUGAR_BEETS 6000 ;
param SubQuotaSellingPrice := WHEAT 170 CORN 150 SUGAR_BEETS 36 ;
param SuperQuotaSellingPrice := WHEAT 0 CORN 0 SUGAR_BEETS 10 ;
param CattleFeedRequirement := WHEAT 200 CORN 240 SUGAR_BEETS 0 ;
# can't purchase beets (no real need, as cattle don't eat them)
param PurchasePrice := WHEAT 238 CORN 210 SUGAR_BEETS 100000 ;
param PlantingCostPerAcre := WHEAT 150 CORN 230 SUGAR_BEETS 260 ;
param Yield := WHEAT 2.5 CORN 3 SUGAR_BEETS 20 ;
Any of these data could be modeled as uncertain, but we will consider only the possibility that the yield per acre could be higher or lower than expected. Assume that there is a probability of 1/3 that the yields will be the average values that were given (i.e., wheat 2.5; corn 3; and beets 20). Assume that there is a 1/3 probability that they will be lower (2, 2.4, 16) and 1/3 probability they will be higher (3, 3.6, 24). We refer to each full set of data as a scenario and collectively we call them a scenario tree. In this case the scenario tree is very simple: there is a root node and three leaf nodes: one corresponding to each scenario. The acreage-to-plant decisions are root node decisions because they must be made without knowing what the yield will be. The other variables are so-called second stage decisions, because they will depend on which scenario is realized.
ScenarioStructure.dat¶
PySP requires that users describe the scenario tree using specific
constructs in a file named ScenarioStructure.dat
; for the farmer
problem, this file can be found in the pysp sub-directory
examples/farmer/scenariodata
that is distributed with PySP.
# IMPORTANT - THE STAGES ARE ASSUMED TO BE IN TIME-ORDER.
set Stages := FirstStage SecondStage ;
set Nodes := RootNode
BelowAverageNode
AverageNode
AboveAverageNode ;
param NodeStage := RootNode FirstStage
BelowAverageNode SecondStage
AverageNode SecondStage
AboveAverageNode SecondStage ;
set Children[RootNode] := BelowAverageNode
AverageNode
AboveAverageNode ;
param ConditionalProbability := RootNode 1.0
BelowAverageNode 0.33333333
AverageNode 0.33333334
AboveAverageNode 0.33333333 ;
set Scenarios := BelowAverageScenario
AverageScenario
AboveAverageScenario ;
param ScenarioLeafNode :=
BelowAverageScenario BelowAverageNode
AverageScenario AverageNode
AboveAverageScenario AboveAverageNode ;
set StageVariables[FirstStage] := DevotedAcreage[*];
set StageVariables[SecondStage] := QuantitySubQuotaSold[*]
QuantitySuperQuotaSold[*]
QuantityPurchased[*];
param StageCost := FirstStage FirstStageCost
SecondStage SecondStageCost ;
This data file is verbose and somewhat redundant, but in most
applications it is generated by software rather than by a person, so
this is not an issue. Generally, the left-most part of each expression
(e.g. ‘’set Stages :=’’) is required and uses reserved words (e.g.,
Stages
) and the other names are supplied by the user (e.g.,
‘’FirstStage’’ could be any name). Every assignment is terminated with a
semi-colon. We will now consider the assignments in this file one at a
time.
The first assignments provides names for the stages and the words “set Stages” are required, as are the := symbols. Any names can be used. In this example, we used “FirstStage” and “SecondStage” but we could have used “EtapPrimero” and “ZweiteEtage” if we had wanted to. Whatever names are given here will continue to be used to refer to the stages in the rest of the file. The order of the names is important. A simple way to think of it is that generally, the names must be in time order (technically, they need to be in order of information discovery, but that is usually time-order). Stages refers to decision stages, which may, or may not, correspond directly with time stages. In the farmer example, decisions about how much to plant are made in the first stage and “decisions” (which are pretty obvious, but which are decision variables nonetheless) about how much to sell at each price and how much needs to be bought are second stage decisions because they are made after the yield is known.
set Stages := FirstStage SecondStage ;
Node names are constructed next. The words “set Nodes” are required, but any names may be assigned to the nodes. In two stage stochastic problems there is a root node, which we chose to name “RootNode” and then there is a node for each scenario.
set Nodes := RootNode
BelowAverageNode
AverageNode
AboveAverageNode ;
Nodes are associated with time stages with an assignment beginning with the required words “param Nodestage.” The assignments must make use of previously defined node and stage names. Every node must be assigned a stage.
param NodeStage := RootNode FirstStage
BelowAverageNode SecondStage
AverageNode SecondStage
AboveAverageNode SecondStage ;
The structure of the scenario tree is defined using assignment of children to each node that has them. Since this is a two stage problem, only the root node has children. The words “param Children” are required for every node that has children and the name of the node is in square brackets before the colon-equals assignment symbols. A list of children is assigned.
set Children[RootNode] := BelowAverageNode
AverageNode
AboveAverageNode ;
The probability for each node, conditional on observing the parent node is given in an assignment that begins with the required words “param ConditionalProbability.” The root node always has a conditional probability of 1, but it must always be given anyway. In this example, the second stage nodes are equally likely.
param ConditionalProbability := RootNode 1.0
BelowAverageNode 0.33333333
AverageNode 0.33333334
AboveAverageNode 0.33333333 ;
Scenario names are given in an assignment that begins with the required words “set Scenarios” and provides a list of the names of the scenarios. Any names may be given. In many applications they are given unimaginative names generated by software such as “Scen1” and the like. In this example, there are three scenarios and the names reflect the relative values of the yields.
set Scenarios := BelowAverageScenario
AverageScenario
AboveAverageScenario ;
Leaf nodes, which are nodes with no children, are associated with scenarios. This assignment must be one-to-one and it is initiated with the words “param ScenarioLeafNode” followed by the colon-equals assignment characters.
param ScenarioLeafNode :=
BelowAverageScenario BelowAverageNode
AverageScenario AverageNode
AboveAverageScenario AboveAverageNode ;
Variables are associated with stages using an assignment that begins with the required words “set StageVariables” and the name of a stage in square brackets followed by the colon-equals assignment characters. Variable names that have been defined in the file ReferenceModel.py can be assigned to stages. Any variables that are not assigned are assumed to be in the last stage. Variable indexes can be given explicitly and/or wildcards can be used. Note that the variable names appear without the prefix “model.” In the farmer example, DevotedAcreage is the only first stage variable.
set StageVariables[FirstStage] := DevotedAcreage[*] ;
set StageVariables[SecondStage] := QuantitySubQuotaSold[*]
QuantitySuperQuotaSold[*]
QuantityPurchased[*] ;
Note
Variable names appear without the prefix “model.”
Note
Wildcards can be used, but fully general Python slicing is not supported.
For reporting purposes, it is useful to define auxiliary variables in
ReferenceModel.py
that will be assigned the cost associated with
each stage. This variables do not impact algorithms, but the values are
output by some software during execution as well as upon completion. The
names of the variables are assigned to stages using the “param
StageCost” assignment. The stages are previously defined in
ScenarioStructure.dat
and the variables are previously defined in
ReferenceModel.py
.
param StageCost := FirstStage FirstStageCost
SecondStage SecondStageCost ;
Scenario data specification¶
So far, we have given a model in the file named ReferenceModel.py
, a
set of deterministic data in the file named ReferenceModel.py
, and a
description of the stochastics in the file named
ScenarioStructure.dat
. All that remains is to give the data for each
scenario. There are two ways to do that in PySP: scenario-based and
node-based. The default is scenario-based so we will describe that
first.
For scenario-based data, the full data for each scenario is given in a
.dat
file with the root name that is the name of the scenario. So,
for example, the file named AverageScenario.dat
must contain all the
data for the model for the scenario named “AvererageScenario.” It turns
out that this file can be created by simply copying the file
ReferenceModel.dat
as shown above because it contains a full set of
data for the “AverageScenario” scenario. The files
BelowAverageScenario.dat
and AboveAverageScenario.dat
will
differ from this file and from each other only in their last line, where
the yield is specified. These three files are distributed with PySP
and are in the pysp sub-directory examples/farmer/scenariodata
along with ScenarioStructure.dat
and ReferenceModel.dat
.
Scenario-based data wastes resources by specifying the same thing over
and over again. In many cases, that does not matter and it is convenient
to have full scenario data files available (for one thing, the scenarios
can easily be run independently using the pyomo
command). However,
in many other settings, it is better to use a node-based specification
where the data that is unique to each node is specified in a .dat file
with a root name that matches the node name. In the farmer example, the
file RootNode.dat
will be the same as ReferenceModel.dat
except
that it will lack the last line that specifies the yield. The files
BelowAverageNode.dat
, AverageNode.dat
, and
AboveAverageNode.dat
will contain only one line each to specify the
yield. If node-based data is to be used, then the
ScenarioStructure.dat
file must contain the following line:
param ScenarioBasedData := False ;
An entire set of files for node-based data for the farmer problem are
distributed with PySP in the sub-directory
examples/farmer/nodedata
Finding Solutions for Stochastic Models¶
PySP provides a variety of tools for finding solutions to stochastic programs.
runef¶
The runef
command puts together the so-called extensive form
version of the model. It creates a large model that has constraints to
ensure that variables at a node have the same value. For example, in the
farmer problem, all of the DevotedAcres
variables must have the same
value regardless of which scenario is ultimately realized. The objective
can be the expected value of the objective function, or the CVaR, or a
weighted combination of the two. Expected value is the default. A full
set of options for runef
can be obtained using the command:
runef --help
The pysp distribution contains the files need to run the farmer example
in the sub-directories to the sub-directory examples/farmer
so
if this is the current directory and if CPLEX is installed, the
following command will cause formation of the EF and its solution using
CPLEX.
runef -m models -i nodedata --solver=cplex --solve
The option -m models
has one dash and is short-hand for the option
--model-directory=models
and note that the full option uses two
dashes. The -i
is equivalent to --instance-directory=
in the
same fashion. The default solver is CPLEX, so the solver option is not
really needed. With the --solve
option, runef would simply write an
.lp data file that could be passed to a solver.
runph¶
The runph command executes an implementation of Progressive Hedging (PH) that is intended to support scripting and extension.
The pysp distribution contains the files need to run the farmer example
in the sub-directories to the sub-directory examples/farmer
so
if this is the current directory and if CPLEX is installed, the
following command will cause PH to execute using the default sub-problem
solver, which is CPLEX.
runph -m models -i nodedata
The option -m models
has one dash and is short-hand for the option
--model-directory=models
and note that the full option uses two
dashes. The -i
is equivalent to --instance-directory=
in the
same fashion.
After about 33 iterations, the algorithm will achieve the default level of convergence and terminate. A lot of output is generated and among the output is the following solution information:
Variable=DevotedAcreage
Index: [CORN] (Scenarios: BelowAverageScenario AverageScenario AboveAverageScenario )
Values: 79.9844 80.0000 79.9768 Max-Min= 0.0232 Avg= 79.9871
Index: [SUGAR_BEETS] (Scenarios: BelowAverageScenario AverageScenario AboveAverageScenario )
Values: 249.9848 249.9770 250.0000 Max-Min= 0.0230 Avg= 249.9873
Index: [WHEAT] (Scenarios: BelowAverageScenario AverageScenario AboveAverageScenario )
Values: 170.0308 170.0230 170.0232 Max-Min= 0.0078 Avg= 170.0256
Cost Variable=FirstStageCost
Tree Node=RootNode (Scenarios: BelowAverageScenario AverageScenario AboveAverageScenario )
Values: 108897.0836 108897.4725 108898.1476 Max-Min= 1.0640 Avg= 108897.5679
For problems with no, or few, integer variables, the default level of convergence leaves root-node variables almost converged. Since the acreage to be planted cannot depend on the scenario that will be realized in the future, the average, which is labeled “Avg” in this output, would be used. A farmer would probably interpret acreages of 79.9871, 249.9873, and 170.0256 to be 80, 250, and 170. In real-world applications, PH is embedded in scripts that produce output in a format desired by a decision maker.
But in real-world applications, the default settings for PH seldom work well enough. In addition to post-processing the output, a number of parameters need to be adjusted and sometimes scripting to extend or augment the algorithm is needed to improve convergence rates. A full set of options can be obtained with the command:
runph --help
Note that there are two dashes before help
.
By default, PH uses quadratic objective functions after iteration zero;
in some settings it may be desirable to linearize the quadratic
terms. This is required to use a solver such as glpk for MIPs because it
does not support quadratic MIPs. The directive
--linearize-nonbinary-penalty-terms=n
causes linearization of the
penalty terms using n pieces. For example, to use glpk on the farmer,
assuming glpk is installed and the command is given when the current
directory is the examples/farmer
, the following command will
use default settings for most parameters and four pieces to approximate
quadratic terms in sub-problems:
runph -i nodedata -m models --solver=glpk --linearize-nonbinary-penalty-terms=4
Use of the linearize-nonbinary-penalty-terms
option requires that
all variables not in the final stage have bounds.
Final Solution¶
At each iteration, PH computes an average for each variable over the nodes of the scenario tree. We refer to this as X-bar. For many problems, particularly those with integer restrictions, X-bar might not be feasible for every scenario unless PH happens to be fully converged (in the primal variables). Consequently, the software computes a solution system X-hat that is more likely to be feasible for every scenario and will be equivalent to X-bar under full convergence. This solution is reported upon completion of PH and its expected value is report if it is feasible for all scenarios.
Methods for computing X-hat are controlled by the --xhat-method
command-line option. For example
--xhat-method=closest-scenario
causes X-hat to be set to the scenario that is closest to X-bar (in a
z-score sense). Other options, such as voting
and rounding
,
assign values of X-bar to X-hat except for binary and general integer
variables, where the values are set by probability weighted voting by
scenarios and rounding from X-bar, respectively.
Solution Output Control¶
To get the full solution, including leaf node solution values, use the
runph
--output-scenario-tree-solution
option.
In both runph
and runef
the solution can be written in csv
format using the
--solution-writer=pysp.plugins.csvsolutionwriter
option.
Summary of PySP File Names¶
PySP scripts such as runef
and runph
require files that specify
the model and data using files with specific names. All files can be in
the current directory, but typically, the file ReferenceModel.py
is
in a directory that is specified using --model-directory=
option
(the short version of this option is -i
) and the data files are in a
directory specified in the --instance-directory=
option (the short
version of this option is -m
).
Note
A file name other than ReferenceModel.py
can be used if the file
name is given in addition to the directory name as an argument to the
--instance-directory
option. For example, on a Windows machine
--instance-directory=models\MyModel.py
would specify the file
MyModel.py
in the local directory models
.
ReferenceModel.py
: A full Pyomo model for a singe scenario. There should be no scenario indexes in this model because they are implicit.ScenarioStructure.dat
: Specifies the nature of the stochastics. It also specifies whether the rest of the data is node-based or scenario-based. It is scenario-based unlessScenarioStructure.dat
contains the line
param ScenarioBasedData := False ;
If scenario-based, then there is a data file for each scenario that
specifies a full set of data for the scenario. The name of the file is
the name of the scenario with .dat
appended. The names of the
scenarios are given in the ScenarioStructure.dat
file.
If node-based, then there is a file with data for each node that
specifies only that data that is unique for the node. The name of the
file is the name of the node with .dat
appended. The names of the
nodes are given in the ScenarioStructure.dat
file.
Solving Sub-problems in Parallel and/or Remotely¶
The Python package called Pyro provides capabilities that are used to
enable PH to make use of multiple solver processes for sub-problems and
allows both runef
and runph
to make use remote solvers. We will
focus on PH in our discussion here.
There are two solver management systems available for runph
, one is
based on a pyro_mip_server
and the other is based on a
phsolverserver
. Regardless of which is used, a name server and a
dispatch server must be running and accessible to the runph
process. The name server is launched using the command pyomo_ns
and
then the dispatch server is launched with dispatch_srvr
. Note that
both commands contain an underscore. Both programs keep running until
terminated by an external signal, so it is common to pipe their output
to a file.
Solvers are controlled by solver servers. The pyro mip solver server is
launched with the command pyro_mip_server
. This command may be
repeated to launch as many solvers as are desired. The runph
then
needs a --solver-manager=pyro
option to signal that runph
should
not launch its own solver, but should send subproblems to be dispatched
to parallel solvers. To summarize the commands:
Once:
pyomo_ns
Once:
dispatch_srvr
Multiple times:
pyro_mip_server
Once:
runph ... --solver-manager=pyro ...
Note
The runph
option --shutdown-pyro
will cause a shutdown signal
to be sent to pyomo_ns
, dispatch_srvr
and all
pyro_mip_server
programs upon termination of runph
.
Instead of using pyro_mip_server
, one can use phsolverserver
in
its place. You can get a list of arguments using pyrosolverserver
--help
, which does not launch a solver server (it just displays help
and terminates). If you use the phsolverserver, then use
--solver-manager=phpyro
as an argument to runph rather than
--solver-manager=pyro
.
Warning
Unlike the normal pyro_mip_server
, there must be one
phsolverserver
for each sub-problem. One can use fewer
phsolverservers than there are scenarios by adding the command-line
option “–phpyro-required-workers=X”. This will partition the jobs
among the available workers,
Generating SMPS Input Files From PySP Models¶
This document explains how to convert a PySP model into a set of files
representing the SMPS format for stochastic linear programs. Conversion
can be performed through the command line by invoking the SMPS converter
using the command python -m pysp.convert.smps
. This command is
available starting with Pyomo version 5.1. Prior to version 5.1, the
same functionality was available via the command pysp2smps
(starting
at Pyomo version 4.2).
SMPS is a standard for expressing stochastic mathematical programs that is based on the ancient MPS format for linear programs, which is matrix-based. Modern algebraic modeling languages such as Pyomo offer a lot of flexibility so it is a challenge to take models expressed in Pyomo/PySP and force them into SMPS format. The conversions can be inefficient and error prone because Pyomo allows flexible expressions and model construction so the resulting matrix may not be the same for each set of input data. We provide tools for conversion to SMPS because some researchers have tools that read SMPS and exploit its limitations on problem structure; however, the user should be aware that the conversion is not always possible.
Currently, these routines only support two-stage stochastic programs. Support for models with more than two time stages will be considered in the future as this tool matures.
Additional Requirements for SMPS Conversion¶
To enable proper conversion of a PySP model to a set of SMPS files, the following additional requirements must be met:
The reference Pyomo model must include annotations that identify stochastic data locations in the second-stage problem.
All model variables must be declared in the ScenarioStructure.dat file.
The set of constraints and variables, and the overall sparsity structure of the objective and constraint matrix must not change across scenarios.
The bulk of this section discusses in-depth the annotations mentioned in
the first point. The second point may come as a surprise to users that
are not aware of the ability to not declare variables in the
ScenarioStructure.dat file. Indeed, for most of the code in PySP, it is
only critical that the variables for which non-anticipativity must be
enforced need to be declared. That is, for a two-stage stochastic
program, all second-stage variables can be left out of the
ScenarioStructure.dat file when using commands such as runef
and
runph
. However, conversion to SMPS format requires all variables to
be properly assigned a decision stage by the user.
Note
Variables can be declared as primary by assigning them to a stage
using the StageVariables
assignment, or declared as auxiliary
variables, which are assigned to a stage using
StageDerivedVariables
assignment. For algorithms such as PH, the
distinction is meaningful and those variables that are fully
determined by primary variables and the data should generally be
assigned to StageDerivedVariables
for their stage.
The third point may also come as a surprise, but the ability to handle a non-uniform problem structure in most PySP tools falls directly from the fact that the non-anticipativity conditions are all that is required in many cases. However, the conversion to SMPS format is based on a matrix representation of the problem where the stochastic coefficients are provided as a set of sparse matrix coordinates. This subsequently requires that the row and column dimensions as well as the sparsity structure of the problem does not change across scenarios.
Annotating Models for SMPS File Generation¶
Annotations are necessary for alerting the SMPS conversion routines of the locations of data that needs to be updated when changing from one scenario to another. Knowing these sparse locations allows decomposition algorithms to employ efficient methods for solving a stochastic program. In order to use the SMPS conversion tool, at least one of the following annotations must be declared on the reference Pyomo model:
StochasticConstraintBoundsAnnotation: indicates the existence of stochastic constraint right-hand-sides (or bounds) in second-stage constraints
StochasticConstraintBodyAnnotation: indicates the existence of stochastic variable coefficients in second-stage constraints
StochasticObjectiveAnnotation: indicates the existence stochastic cost coefficients in the second-stage cost function
These will be discussed in further detail in the remaining sections. The following code snippet demonstrates how to import these annotations and declare them on a model.
from pysp import annotations
model.stoch_rhs = annotations.StochasticConstraintBoundsAnnotation()
model.stoch_matrix = annotations.StochasticConstraintBodyAnnotation()
model.stoch_objective = annotations.StochasticObjectiveAnnotation()
Populating these annotations with entries is optional, and simply declaring them on the reference Pyomo model will alert the SMPS conversion routines that all coefficients appearing on the second-stage model should be assumed stochastic. That is, adding the lines in the previous code snippet alone implies that: (i) all second-stage constraints have stochastic bounds, (ii) all first- and second-stage variables appearing in second-stage constraints have stochastic coefficients, and (iii) all first- and second-stage variables appearing in the objective have stochastic coefficients.
PySP can attempt to determine the stage-ness of a constraint by examining the set of variables that appear in the constraint expression. E.g., a first-stage constraint is characterized as having only first-stage variables appearing in its expression. A second-stage constraint has at least one second-stage variable appearing in its expression. The stage of a variable is declared in the scenario tree provided to PySP. This method of constraint stage classification is not perfect. That is, one can very easily define a model with a constraint that uses only first-stage variables in an expression involving stochastic data. This constraint would be incorrectly identified as first-stage by the method above, even though the existence of stochastic data necessarily implies it is second-stage. To deal with cases such as this, an additional annotation is made available that is named ConstraintStageAnnotation. This annotation will be discussed further in a later section.
It is often the case that relatively few coefficients on a stochastic
program change across scenarios. In these situations, adding explicit
declarations within these annotations will allow for a more sparse
representation of the problem and, consequently, more efficient solution
by particular decomposition methods. Adding declarations to these
annotations is performed by calling the declare
method, passing some
component as the initial argument. Any remaining argument requirements
for this method are specific to each annotation. Valid types for the
component argument typically include:
Constraint
: includes single constraint objects as well as constraint containersObjective
: includes single objective objects as well as objective containersBlock
: includes Pyomo models as well as single block objects and block containers
Any remaining details for adding declarations to the annotations mentioned thus far will be discussed in later sections. The remainder of this section discusses the semantics of these declarations based on the type for the component argument.
When the declare
method is called with a component such as an
indexed Constraint
or a Block
(model), the SMPS conversion
routines will interpret this as meaning all constraints found within
that indexed Constraint
or on that Block
(that have not been
deactivated) should be considered. As an example, we consider the
following partially declared concrete Pyomo model:
model = pyo.ConcreteModel()
# data that is initialized on a per-scenario basis
p = 1.0
q = 2.0
# variables declared as second-stage on the
# PySP scenario tree
model.z = pyo.Var()
model.y = pyo.Var()
# indexed constraint
model.r_index = pyo.Set(initialize=[3, 6, 9])
def r_rule(model, i):
return pyo.inequality(p + i, 1*model.z + 5*model.y, 10 + q + i)
model.r = pyo.Constraint(model.r_index, rule=r_rule)
# singleton constraint
model.c = pyo.Constraint(expr= p*model.z >= 1)
# a sub-block with a singleton constraint
model.b = pyo.Block()
model.b.c = pyo.Constraint(expr= q*model.y >= 1)
Here the local Python variables p
and q
serve as placeholders
for data that changes with each scenario.
The following are equivalent annotations of the model, each declaring all of the constraints shown above as having stochastic right-hand-side data:
Implicit form
model.stoch_rhs = annotations.StochasticConstraintBoundsAnnotation()
Implicit form for
Block
(model) assignmentmodel.stoch_rhs = annotations.StochasticConstraintBoundsAnnotation() model.stoch_rhs.declare(model)
Explicit form for singleton constraint with implicit form for indexed constraint and sub-block
model.stoch_rhs = annotations.StochasticConstraintBoundsAnnotation() model.stoch_rhs.declare(model.r) model.stoch_rhs.declare(model.c) model.stoch_rhs.declare(model.b)
Explicit form for singleton constraints at the model and sub-block level with implicit form for indexed constraint
model.stoch_rhs = annotations.StochasticConstraintBoundsAnnotation() model.stoch_rhs.declare(model.r) model.stoch_rhs.declare(model.c) model.stoch_rhs.declare(model.b.c)
Fully explicit form for singleton constraints as well as all indices of indexed constraint
model.stoch_rhs = annotations.StochasticConstraintBoundsAnnotation() model.stoch_rhs.declare(model.r[3]) model.stoch_rhs.declare(model.r[6]) model.stoch_rhs.declare(model.r[9]) model.stoch_rhs.declare(model.c) model.stoch_rhs.declare(model.b.c)
Note that the equivalence of the first three bullet forms to the last
two bullet forms relies on the following conditions being met: (1)
model.z
and model.y
are declared on the second stage of the PySP
scenario tree and (2) at least one of these second-stage variables
appears in each of the constraint expressions above. Together, these two
conditions cause each of the constraints above to be categorized as
second-stage; thus, causing them to be considered by the SMPS conversion
routines in the implicit declarations used by the first three bullet
forms.
Warning
Pyomo simplifies product expressions such that terms with 0 coefficients are removed from the final expression. This can sometimes create issues with determining the correct stage classification of a constraint as well as result in different sparsity patterns across scenarios. This issue is discussed further in the later section entitled Edge-Cases.
When it comes to catching errors in model annotations, there is a minor difference between the first bullet form from above (empty annotation) and the others. In the empty case, PySP will use exactly the set of second-stage constraints it is aware of. This set will either be determined through inspection of the constraint expressions or through the user-provided constraint-stage classifications declared using the ConstraintStageAnnotation annotation type. In the case where the stochastic annotation is not empty, PySP will verify that all constraints declared within it belong to the set of second-stage constraints it is aware of. If this verification fails, an error will be reported. This behavior is meant to aid users in debugging problems associated with non-uniform sparsity structure across scenarios that are, for example, caused by 0 coefficients in product expressions.
Annotations on AbstractModel Objects
Pyomo models defined using the AbstractModel
object require the
modeler to take further steps when making these annotations. In the
AbstractModel
setting, these assignments must take place within a
BuildAction
, which is executed only after the model has been
constructed with data. As an example, the last bullet form from the
previous section could be written in the following way to allow
execution with either an AbstractModel
or a ConcreteModel
:
def annotate_rule(m):
m.stoch_rhs = annotations.StochasticConstraintBoundsAnnotation()
m.stoch_rhs.declare(m.r[3])
m.stoch_rhs.declare(m.r[6])
m.stoch_rhs.declare(m.r[9])
m.stoch_rhs.declare(m.c)
m.stoch_rhs.declare(m.b.c)
model.annotate = pyo.BuildAction(rule=annotate_rule)
Note that the use of m
rather than model
in the
annotate_rule
function is meant to draw attention to the fact that
the model object being passed into the function as the first argument
may not be the same object as the model outside of the function. This is
in fact the case in the AbstractModel
setting, whereas for the
ConcreteModel
setting they are the same object. We often use
model
in both places to avoid errors caused by forgetting to use the
correct object inside the function (Python scoping rules handle the
rest). Also note that a BuildAction
must be declared on the model
after the declaration of any components being accessed inside its rule
function.
Stochastic Constraint Bounds (RHS)
If stochastic elements appear on the right-hand-side of constraints (or
as constants in the body of constraint expressions), these locations
should be declared using the StochasticConstraintBoundsAnnotation annotation
type. When components are declared with this annotation, there are no
additional required arguments for the declare
method. However, to
allow for more flexibility when dealing with double-sided inequality
constraints, the declare
method can be called with at most one of
the keywords lb
or ub
set to False
to signify that one of
the bounds is not stochastic. The following code snippet shows example
declarations with this annotation for various constraint types.
# declare the annotation
model.stoch_rhs = annotations.StochasticConstraintBoundsAnnotation()
# equality constraint
model.c_eq = pyo.Constraint(expr= model.y == q)
model.stoch_rhs.declare(model.c_eq)
# range inequality constraint with stochastic upper bound
model.c_ineq = pyo.Constraint(expr= pyo.inequality(0, model.y, p))
model.stoch_rhs.declare(model.c_ineq, lb=False)
# indexed constraint using a BuildAction
model.C_index = pyo.RangeSet(1,3)
def C_rule(model, i):
if i == 1:
return model.y >= i * q
else:
return pyo.Constraint.Skip
model.C = pyo.Constraint(model.C_index, rule=C_rule)
def C_annotate_rule(model, i):
if i == 1:
model.stoch_rhs.declare(model.C[i])
else:
pass
model.C_annotate = pyo.BuildAction(model.C_index, rule=C_annotate_rule)
Note that simply declaring the StochasticConstraintBoundsAnnotation
annotation type and leaving it empty will alert the SMPS conversion
routines that all constraints identified as second-stage should be
treated as having stochastic right-hand-side data. Calling the
declare
method on at least one component implies that the set of
constraints considered should be limited to what is declared within the
annotation.
Stochastic Constraint Matrix
If coefficients of variables change in the second-stage constraint
matrix, these locations should be declared using the
StochasticConstraintBodyAnnotation annotation type. When components are
declared with this annotation, there are no additional required
arguments for the declare
method. Calling the declare
method
with the single component argument signifies that all variables
encountered in the constraint expression (including first- and
second-stage variables) should be treated as having stochastic
coefficients. This can be limited to a specific subset of variables by
calling the declare
method with the variables
keyword set to an
explicit list of variable objects. The following code snippet shows
example declarations with this annotation for various constraint types.
model = pyo.ConcreteModel()
# data that is initialized on a per-scenario basis
p = 1.0
q = 2.0
# a first-stage variable
model.x = pyo.Var()
# a second-stage variable
model.y = pyo.Var()
# declare the annotation
model.stoch_matrix = annotations.StochasticConstraintBodyAnnotation()
# a singleton constraint with stochastic coefficients
# both the first- and second-stage variable
model.c = pyo.Constraint(expr= p*model.x + q*model.y == 1)
model.stoch_matrix.declare(model.c)
# an assignment that is equivalent to the previous one
model.stoch_matrix.declare(model.c, variables=[model.x, model.y])
# a singleton range constraint with a stochastic coefficient
# for the first-stage variable only
model.r = pyo.Constraint(expr=pyo.inequality(0, p*model.x - 2.0*model.y, 10))
model.stoch_matrix.declare(model.r, variables=[model.x])
As is the case with the StochasticConstraintBoundsAnnotation annotation
type, simply declaring the StochasticConstraintBodyAnnotation
annotation type and leaving it empty will alert the SMPS conversion
routines that all constraints identified as second-stage should be
considered, and, additionally, that all variables encountered in these
constraints should be considered to have stochastic
coefficients. Calling the declare
method on at least one component
implies that the set of constraints considered should be limited to what
is declared within the annotation.
Stochastic Objective Elements
If the cost coefficients of any variables are stochastic in the
second-stage cost expression, this should be noted using the
StochasticObjectiveAnnotation annotation type. This annotation
uses the same semantics for the declare
method as the
StochasticConstraintBodyAnnotation annotation type, but with one
additional consideration regarding any constants in the objective
expression. Constants in the objective are treated as stochastic and
automatically handled by the SMPS code. If the objective expression does
not contain any constant terms or these constant terms do not change
across scenarios, this behavior can be disabled by setting the keyword
include_constant
to False
in a call to the declare
method.
# declare the annotation
model.stoch_objective = annotations.StochasticObjectiveAnnotation()
model.FirstStageCost = pyo.Expression(expr= 5.0*model.x)
model.SecondStageCost = pyo.Expression(expr= p*model.x + q*model.y)
model.TotalCost = pyo.Objective(expr= model.FirstStageCost + model.SecondStageCost)
# each of these declarations is equivalent for this model
model.stoch_objective.declare(model.TotalCost)
model.stoch_objective.declare(model.TotalCost, variables=[model.x, model.y])
Similar to the previous annotation type, simply declaring the StochasticObjectiveAnnotation annotation type and leaving it empty will alert the SMPS conversion routines that all variables appearing in the single active model objective expression should be considered to have stochastic coefficients.
Edge Cases
The section discusses various points that may give users some trouble, and it attempts to provide more details about the common pitfalls associated with translating a PySP model to SMPS format.
Moving a Stochastic Objective to the Constraint Matrix
It is often the case that decomposition algorithms theoretically support stochastic cost coefficients but the software implementation has not yet added support for them. This situation is easy to work around in PySP. One can simply augment the model with an additional constraint and variable that computes the objective, and then use this variable in the objective rather than directly using the second-stage cost expression. Consider the following reference Pyomo model that has stochastic cost coefficients for both a first-stage and a second-stage variable in the second-stage cost expression:
>>> # suppress duplicate object warning
>>> del model.TotalCost
# define the objective as the sum of the stage-cost expressions
model.TotalCost = pyo.Objective(expr= model.FirstStageCost + model.SecondStageCost)
# declare that model.x and model.y have stochastic cost
# coefficients in the second stage
model.stoch_objective = annotations.StochasticObjectiveAnnotation()
model.stoch_objective.declare(model.TotalCost, variables=[model.x, model.y])
The code snippet below re-expresses this model using an objective
consisting of the original first-stage cost expression plus a
second-stage variable SecondStageCostVar
that represents the
second-stage cost. This is enforced by restricting the variable to be
equal to the second-stage cost expression using an additional equality
constraint named ComputeSecondStageCost
. Additionally, the
StochasticObjectiveAnnotation annotation type is replaced with
the StochasticConstraintBodyAnnotation annotation type.
# set the variable SecondStageCostVar equal to the
# expression SecondStageCost using an equality constraint
model.SecondStageCostVar = pyo.Var()
model.ComputeSecondStageCost = pyo.Constraint(expr= model.SecondStageCostVar == model.SecondStageCost)
# declare that model.x and model.y have stochastic constraint matrix
# coefficients in the ComputeSecondStageCost constraint
model.stoch_matrix = annotations.StochasticConstraintBodyAnnotation()
model.stoch_matrix.declare(model.ComputeSecondStageCost, variables=[model.x, model.y])
Stochastic Constant Terms
The standard description of a linear program does not allow for a constant term in the objective function because this has no weight on the problem solution. Additionally, constant terms appearing in a constraint expression must be lumped into the right-hand-side vector. However, when modeling with an AML such as Pyomo, constant terms very naturally fall out of objective and constraint expressions.
If a constant terms falls out of a constraint expression and this term changes across scenarios, it is critical that this is accounted for by including the constraint in the StochasticConstraintBoundsAnnotation annotation type. Otherwise, this would lead to an incorrect representation of the stochastic program in SMPS format. As an example, consider the following:
# a param initialized with scenario-specific data
model.p = pyo.Param(mutable=True)
# a second-stage constraint with a stochastic upper bound
# hidden in the left-hand-side expression
def d_rule(m):
return (m.x - m.p) + m.y <= 10
model.d = pyo.Constraint(rule=d_rule)
Note that in the expression for constraint c
, there is a fixed
parameter p
involved in the variable expression on the
left-hand-side of the inequality. When an expression is written this
way, it can be easy to forget that the value of this parameter will be
pushed to the bound of the constraint when it is converted into linear
canonical form. Remember to declare these constraints within the
StochasticConstraintBoundsAnnotation annotation type.
A constant term appearing in the objective expression presents a similar issue. Whether or not this term is stochastic, it must be dealt with when certain outputs expect the problem to be expressed as a linear program. The SMPS code in PySP will deal with this situation for you by implicitly adding a new second-stage variable to the problem in the final output file that uses the constant term as its coefficient in the objective and that is fixed to a value of 1.0 using a trivial equality constraint. The default behavior when declaring the StochasticObjectiveAnnotation annotation type will be to assume this constant term in the objective is stochastic. This helps ensure that the relative scenario costs reported by algorithms using the SMPS files will match that of the PySP model for a given solution. When moving a stochastic objective into the constraint matrix using the method discussed in the previous subsection, it is important to be aware of this behavior. A stochastic constant term in the objective would necessarily translate into a stochastic constraint right-hand-side when moved to the constraint matrix.
Stochastic Variable Bounds
Although not directly supported, stochastic variable bounds can be expressed using explicit constraints along with the StochasticConstraintBoundsAnnotation annotation type to achieve the same effect.
Problems Caused by Zero Coefficients
Expressions that involve products with some terms having 0 coefficients
can be problematic when the zeros can become nonzero in certain
scenarios. This can cause the sparsity structure of the LP to change
across scenarios because Pyomo simplifies these expressions when they
are created such that terms with a 0 coefficient are dropped. This can
result in an invalid SMPS conversion. Of course, this issue is not
limited to explicit product expressions, but can arise when the user
implicitly assigns a variable a zero coefficient by outright excluding
it from an expression. For example, both constraints in the following
code snippet suffer from this same underlying issue, which is that the
variable model.y
will be excluded from the constraint expressions in
a subset of scenarios (depending on the value of q
) either directly
due to a 0 coefficient in a product expressions or indirectly due to
user-defined logic that is based off of the values of stochastic data.
q = 0
model.c1 = pyo.Constraint(expr= p * model.x + q * model.y == 1)
def c2_rule(model):
expr = p * model.x
if q != 0:
expr += model.y
return expr >= 0
model.c2 = pyo.Constraint(rule=c2_rule)
The SMPS conversion routines will attempt some limited checking to help prevent this kind of situation from silently turning the SMPS representation to garbage, but it must ultimately be up to the user to ensure this is not an issue. This is in fact the most challenging aspect of converting PySP’s AML-based problem representation to the structure-preserving LP representation used in the SMPS format.
One way to deal with the 0 coefficient issue, which works for both cases
discussed in the example above, is to create a zero Expression
object. E.g.,
model.zero = pyo.Expression(expr=0)
This component can be used to add variables to a linear expression so that the resulting expression retains a reference to them. This behavior can be verified by examining the output from the following example:
# an expression that does NOT retain model.y
>>> print((model.x + 0 * model.y).to_string())
x
# an equivalent expression that DOES retain model.y
>>> print((model.x + model.zero * model.y).to_string())
x + 0.0*y
# an equivalent expression that does NOT retain model.y (so beware)
>>> print((model.x + 0 * model.zero * model.y).to_string())
x
Generating SMPS Input Files¶
This section explains how the SMPS conversion utilities available in
PySP can be invoked from the command line. Starting with Pyomo version
5.1, the SMPS writer can be invoked using the command python -m
pysp.convert.smps
. Prior to version 5.1, this functionality was
available via the pysp2smps
command (starting at Pyomo version
4.2). Use the --help
option with the main command to see a detailed
description of the command-line options available:
$ python -m pysp.convert.smps --help
Next, we discuss some of the basic inputs to this command.
Consider the baa99 example inside the baa99
subdirectory that
is distributed with the PySP examples (examples/baa99
). Both
the reference model and the scenario tree structure are defined in the
file ReferenceModel.py
using PySP callback functions. This model has
been annotated to enable conversion to the SMPS format. Assuming one is
in this example’s directory, SMPS files can be generated for the model
by executing the following shell command:
$ python -m pysp.convert.smps -m ReferenceModel.py --basename baa99 \
--output-directory sdinput/baa99
Assuming successful execution, this would result in the following files being created:
sdinput/baa99/baa99.cor
sdinput/baa99/baa99.tim
sdinput/baa99/baa99.sto
sdinput/baa99/baa99.cor.symbols
The first file is the core problem file written in MPS format. The second file indicates at which row and column the first and second time stages begin. The third file contains the location and values of stochastic data in the problem for each scenario. This file is generated by merging the individual output for each scenario in the scenario tree into separate BLOCK sections. The last file contains a mapping for non-anticipative variables from the symbols used in the above files to a unique string that can be used to recover the variable on any Pyomo model. It is mainly used by PySP’s solver interfaces to load a solver solution.
To ensure that the problem structure is the same and that all locations
of stochastic data have been annotated properly, the script creates
additional auxiliary files that are compared across scenarios. The
command-line option --keep-auxiliary-files
can be used to retain the
auxiliary files that were generated for the template scenario used to
write the core file. When this option is used with the above example,
the following additional files will appear in the output directory:
sdinput/baa99/baa99.mps.det
sdinput/baa99/baa99.sto.struct
sdinput/baa99/baa99.row
sdinput/baa99/baa99.col
The .mps.det
file is simply the core file for the reference scenario
with the values for all stochastic coefficients set to zero. If this
does not match for every scenario, then there are places in the model
that still need to be declared on one or more of the stochastic data
annotations. The .row
and the .col
files indicate the ordering
of constraints and variables, respectively, that was used to write the
core file. The .sto.struct
file lists the nonzero locations of the
stochastic data in terms of their row and column location in the core
file. These files are created for each scenario instance in the scenario
tree and placed inside of a subdirectory named scenario_files
within
the output directory. These files will be removed removed unless
validation fails or the --keep-scenario-files
option is used.
The SMPS writer also supports parallel execution. This can significantly reduce the overall time required to produce the SMPS files when there are many scenarios. Parallel execution using PySP’s Pyro-based tools can be performed using the steps below. Note that each of these commands can be launched in the background inside the same shell or in their own separate shells.
Start the Pyro name server:
$ pyomo_ns -n localhost
Start the Pyro dispatch server:
$ dispatch_srvr -n localhost --daemon-host localhost
Start 8 ScenarioTree Servers (for the 625 baa99 scenarios)
$ mpirun -np 8 scenariotreeserver --pyro-host=localhost
Run
python -m pysp.convert.smps
using the Pyro ScenarioTree Manager$ python -m pysp.convert.smps -m ReferenceModel.py --basename baa99 \ --output-directory sdinput/baa99 \ --pyro-required-scenariotreeservers=8 \ --pyro-host=localhost --scenario-tree-manager=pyro
An annotated version of the farmer example is also provided. The model
file can be found in the examples/farmer/smps_model
examples
subdirectory. Note that the scenario tree for this model is defined in a
separate file. When invoking the SMPS writer, a scenario tree structure
file can be provided via the --scenario-tree-location (-s)
command-line option. For example, assuming one is in the
farmer
subdirectory, the farmer model can be converted to SMPS
files using the command:
$ python -m pysp.convert.smps -m smps_model/ReferenceModel.py \
-s scenariodata/ScenarioStructure.dat --basename farmer \
--output-directory sdinput/farmer
Note that, by default, the files created by the SMPS writer use
shortened symbols that do not match the names of the variables and
constraints declared on the Pyomo model. This is for efficiency reasons,
as using fully qualified component names can result in significantly
larger files. However, it can be useful in debugging situations to
generate the SMPS files using the original component names. To do this,
simply add the command-line option --symbolic-solver-labels
to the
command string.
The SMPS writer supports other formats for the core problem file (e.g.,
the LP format). The command-line option --core-format
can be used to
control this setting. Refer to the command-line help string for more
information about the list of available format.
Generating DDSIP Input Files From PySP Models¶
PySP provides support for creating DDSIP inputs, and some support for reading DDSIP solutions back into PySP is under development. Use of these utilties requires additional model annotations that declare the location of stochastic data coefficients. See the section on converting PySP models to SMPS for more information.
To access the DDSIP writer via the command line, use python
-m pysp.convert.ddsip
. To access the full solver interface to
DDSIP, which writes the input files, invokes the DDSIP solver, and reads
the solution, use python -m pysp.solvers.ddsip
. For example,
to get a list of command arguments, use:
$ python -m pysp.convert.ddsip --help
Note
Not all of the command arguments are relevant for DDSIP.
For researchers that simply want to write out the files needed by DDSIP,
the --output-directory
option can be used with the DDSIP writer to
specifiy the directory where files should be created. The DDSIP solver
interface creates these files in a temporary directory. To have the
DDSIP solver interface retain these files after it exits, use the
--keep-solver-files
command-line option. The following example
invokes the DDSIP solver on the networkflow example that ships with
PySP. In order to test it, one must first cd
into to the networkflow
example directory and then execute the command:
$ python -m pysp.solvers.ddsip \
-s 1ef10 -m smps_model --solver-options="NODELIM=1”
The --solver-options
command line argument can be used set the
values of any DDSIP options that are written to the DDSIP configuration
file; multiple options should be space-separated. See DDSIP
documentation for a list of options.
Here is the same example modified to simply create the DDSIP input files
in an output directory named ddsip_networkflow
:
$ python -m pysp.convert.ddsip \
-s 1ef10 -m smps_model --output-directory ddsip_networkflow \
--symbolic-solver-labels
The option --symbolic-solver-labels
tells the DDSIP writer to
produce the file names using symbols that match names on the original
Pyomo model. This can significantly increase file size, so it is not
done by default. When the DDSIP writer is invoked, a minimal DDSIP
configuration file is created in the output directory that specifies the
required problem structure information. Any additional DDSIP options
must be manually added to this file by the user.
As with the SMPS writer, the DDSIP writer and solver interface support PySP’s Pyro-based parallel scenario tree management system. See the section on the SMPS writer for a description of how to use this functionality.
PySP in scripts¶
See rapper: a PySP wrapper for information about putting Python scripts around PySP functionality.
Introduction to Using Concrete Models with PySP¶
The concrete interface to PySP requires a function that can return a
concrete model for a given scenario. Optionally, a function that
returns a scenario tree can be provided; however, a
ScenarioStructure.dat
file is also an option. This very
terse introduction might help you get started using
concrete models with PySP.
Scenario Creation Function¶
There is a lot of flexibility in how this function is implemented, but the path of least resistance is
>>> def pysp_instance_creation_callback(scenario_tree_model,
... scenario_name,
... node_names):
... pass
In many applications, only the scenario_name
argument is
used. Its purpose is almost always to determine what data
to use when populating the scenario instance. Note that in
older examples, the scenario_tree_model
argument is not
present.
An older example of this function can be seen in
examples/farmer/concrete/ReferenceModel.py
Note that this example does not have a function to return
a scenario tree, so it can be solved from the
examples/farmer
directory with a command
like:
- ::
runef -m concrete/ReferenceModel.py -s scenariodata/ScenarioStructure.dat –solve
Note
If, for some reason, you want to use the concrete interface for PySP for an AbstractModel
, the body of the function might be something like:
>>> instance = model.create_instance(scenario_name+".dat")
>>> return instance
assuming that model
is defined as an AbstractModel
in the namespace
of the file.
Scenario Tree Creation Function¶
There are many options for a function to return a scenario tree. The path
of least resistance is to name the function pysp_scenario_tree_model_callback
with no arguments.
One example is shown in
examples/farmer/concreteNetX/ReferenceModel.py
It can be solved from the
examples/farmer
directory with a command
like:
- ::
runef -m concreteNetX/ReferenceModel.py –solve