Model#

The model class is used to collect equations into groups and to label them so that they can be solved. It also allows specifying the problem type (e.g. LP, MIP etc.), sense of the problem (MIN, MAX, FEASIBILITY) and the objective of the problem at hand.

The overall syntax of a model is as follows:

from gamspy import Container, Variable, Equation, Model, Sense, Problem

m = Container()

z = Variable(m, "z") # objective variable
e1 = Equation(m, "e1")
e1[...] = <definition_of_the_equation>
e2 = Equation(m, "e2")
e2[...] = <definition_of_the_equation>

example_model = Model(m, name="dummy", equations=[e1,e2], problem=Problem.LP, sense=Sense.Max, objective=z)

Note

In case the name is not provided, it will be autogenerated.

Classification of Models#

Various types of problems can be solved with GAMS. Note that the type of the model must be known before it may be solved. The model types are briefly discussed in this section. GAMS checks that the model is in fact the type the user thinks it is, and issues explanatory error messages if it discovers a mismatch - for instance, that a supposedly linear model contains nonlinear terms. Some problems may be solved in more than one way, and the user has to choose which way to use. For instance, if there are binary or integer variables in the model, it can be solved either as a MIP or as a RMIP.

Model Type

Model Type Description

LP

Linear Program

NLP

Nonlinear Program

QCP

Quadratically Constrained Program

DNLP

Discontinuous Nonlinear Program

MIP

Mixed Integer Program

RMIP

Relaxed Mixed Integer Program

MINLP

Mixed Integer Nonlinear Program

RMINLP

Relaxed Mixed Integer Nonlinear Program

MIQCP

Mixed Integer Quadratically Constrained Program

RMIQCP

Relaxed Mixed Integer Quadratically Constrained Program

MCP

Mixed Complementarity Problem

CNS

Constrained Nonlinear System

MPEC

Mathematical Programs with Equilibrium Constraints

RMPEC

Relaxed Mathematical Program with Equilibrium Constraints

EMP

Extended Mathematical Program

MPSGE

General Equilibrium

All model types are exposed with Problem enum but the problem type can be specified as a string as well.

Also the direction types of the optimization (MIN, MAX, or FEASIBILITY) are exposed with Sense enum but it can be specified as a string similarly.

Matches for MCP Models#

Mixed Complementarity Problem (MCP) models can be defined as pair-wise complementarities between variables and equations. Model accepts these pair-wise complementarities through its matches argument in its constructor.

p = Variable(m, "p", type=VariableType.POSITIVE, domain=c)
y = Variable(m, "y", type=VariableType.POSITIVE, domain=s)
i = Variable(m, "i", type=VariableType.POSITIVE, domain=h)

mkt = Equation(m, "mkt", domain=c)
profit = Equation(m, "profit", domain=s)
income = Equation(m, "income", domain=h)

mkt[c] = Sum(s, a[c, s] * y[s]) + Sum(h, e[c, h]) >= Sum(
    h.where[esub[h] != 1],
    (i[h] / Sum(cc, alpha[cc, h] * p[cc] ** (1 - esub[h])))
    * alpha[c, h]
    * (1 / p[c]) ** esub[h],
) + Sum(h.where[esub[h] == 1], i[h] * alpha[c, h] / p[c])

profit[s] = -Sum(c, a[c, s] * p[c]) >= 0
income[h] = i[h] >= Sum(c, p[c] * e[c, h])

hansen = Model(
    m,
    "hansen",
    problem=Problem.MCP,
    matches={mkt: p, profit: y, income: i},
)

One does not have to provide equations that are provided in the matches in the equations argument. An example MCP model can be found in the model library: HANSMCP.

Model Attributes#

Models have attributes that hold a variety of information, including

  • information about the results of a solve performed, a solve statement, the solution of a model,

  • information about certain features to be used by GAMS or the solver,

  • information passed to GAMS or the solver specifying various settings that are also subject to option statements.

Model Attribute

Description

num_domain_violations

Number of domain violations

algorithm_time

Solver dependent timing information

total_solve_time

Elapsed time it took to execute a solve statement in total

total_solver_time

Elapsed time taken by the solver only

num_iterations

Number of iterations used

marginals

Indicator for marginals present

max_infeasibility

Maximum of infeasibilities

mean_infeasibility

Mean of infeasibilities

status

Integer number that indicates the model status

num_nodes_used

Number of nodes used by the MIP solver

num_dependencies

Number of dependencies in a CNS model

num_discrete_variables

Number of discrete variables

num_infeasibilities

Number of infeasibilities

num_nonlinear_insts

Number of nonlinear instructions

num_nonlinear_zeros

Number of nonlinear nonzeros

num_nonoptimalities

Number of nonoptimalities

num_nonzeros

Number of nonzero entries in the model coefficient matrix

num_mcp_redefinitions

Number of MCP redefinitions

num_variables

Number of variables

num_bound_projections

Number of bound projections during model generation

objective_estimation

Estimate of the best possible solution for a mixed-integer model

objective_value

Objective function value

used_model_type

Integer number that indicates the used model type

model_generation_time

Time GAMS took to generate the model in wall-clock seconds

solve_model_time

Time the solver used to solve the model in seconds

sum_infeasibilities

Sum of infeasibilities

solve_status

Indicates the solver termination condition

solver_version

Solver version

Solving a Model#

Model has a function named solve that allows user to solve the specified model.

from gamspy import Container, Variable, Equation, Model, Sense, Problem, Options

m = Container()

z = Variable(m, "z") # objective variable
e1 = Equation(m, "e1")
e1[...] = <definition_of_the_equation>
e2 = Equation(m, "e2")
e2[...] = <definition_of_the_equation>

model = Model(m, "dummy", equations=[e1,e2], problem=Problem.LP, sense=Sense.Max, objective=z)
model.solve(solver="CONOPT", options=Options(iteration_limit=2), solver_options={"rtmaxv": "1.e12"})

In most cases, calling the solve function of your model without any parameters is sufficient. In this scenario, the default solver depending on the problem type, default options will be used. But for users who requires a higher level of control can set the solver to be used, general options and solver specific options. All installed solvers on your system can be queried by running the following command:

gamspy list solvers

If you want to get all available solvers that you can install and use, the following command would give you the list of solvers that are available.:

gamspy list solvers -a

Redirecting Output#

The output of GAMS after solving the model can be redirected to a file or to standard input by specifying the output parameter of the solve.:

from gamspy import Container, Variable, Equation, Model, Sense, Problem
import sys

m = Container()

z = Variable(m, "z") # objective variable
e1 = Equation(m, "e1")
e1[...] = <definition_of_the_equation>
e2 = Equation(m, "e2")
e2[...] = <definition_of_the_equation>

model = Model(m, "dummy", equations=[e1,e2], problem=Problem.LP, sense=Sense.Max, objective=z)

# redirect output to stdout
model.solve(output=sys.stdout)

# redirect output to a file
with open("my_out_file", "w") as file:
    model.solve(output=file)

Solving Locally#

Models are solved locally (on your machine) by default.

Solving with GAMS Engine#

Synchronous Solve#

In order to send your model to be solved to GAMS Engine, you need to define the configuration of GAMS Engine. This can be done by importing EngineClient and creating an instance. Then, the user can pass it to the solve method and specify the backend as engine.

from gamspy import Container, Variable, Equation, Model, Sense, Problem, EngineClient

m = Container()

z = Variable(m, "z") # objective variable
e1 = Equation(m, "e1")
e1[...] = <definition_of_the_equation>
e2 = Equation(m, "e2")
e2[...] = <definition_of_the_equation>

model = Model(m, "dummy", equations=[e1,e2], problem=Problem.LP, sense=Sense.Max, objective=z)

client = EngineClient(
    host=os.environ["ENGINE_URL"],
    username=os.environ["ENGINE_USER"],
    password=os.environ["ENGINE_PASSWORD"],
    namespace=os.environ["ENGINE_NAMESPACE"],
)
model.solve(solver="CONOPT", backend="engine", client=client)

Note

Extra model file paths that are provided through extra_model_files argument of EngineClient must be relative to the working directory. For example, if your working directory is “/foo/bar”, your extra model file path cannot be “/foo”.

Asynchronous Solve#

If you just want to send your jobs to GAMS Engine without blocking until the results are received, is_blocking parameter can be set to False in EngineClient.

Tokens of the submitted jobs are stored in client.tokens

from gamspy import Container, Variable, Equation, Model, Sense, Problem, EngineClient
m = Container()
...
...
<define_your_model>
...
...
client = EngineClient(
    host=os.environ["ENGINE_URL"],
    username=os.environ["ENGINE_USER"],
    password=os.environ["ENGINE_PASSWORD"],
    namespace=os.environ["ENGINE_NAMESPACE"],
)

for _ in range(3):
    ...
    ...
    <changes_in_your_model>
    ...
    ...
    model.solve(backend="engine", client=client)

print(client.tokens) # This prints all tokens for the submitted jobs

The results of the non-blocking jobs can be retrieved later. For example if want to retrieve the results of the last submitted job, we can do that following:

token = client.tokens[-1]
client.job.get_results(token, working_directory="out_dir")

The results would be downloaded to the given working directory. The downloaded gdx file will have the same name with m.gdxOutputPath(). Then, if one wants to read the results, they can simply create a new Container and read the results from the downloaded gdx file:

gdx_out_path = os.path.join("out_dir", os.path.basename(m.gdxOutputPath()))
container = Container(load_from=gdx_out_path)
# or
container = Container()
container.read(gdx_out_path)

Solving with NEOS Server#

Synchronous Solve#

In order to send your model to be solved to NEOS Server, you need to create a NeosClient. This can be done by importing NeosClient and creating an instance. Then, the user can pass it to the solve method and specify the backend as neos.

from gamspy import Container, Variable, Equation, Model, Sense, Problem, NeosClient

m = Container()

z = Variable(m, "z") # objective variable
e1 = Equation(m, "e1")
e1[...] = <definition_of_the_equation>
e2 = Equation(m, "e2")
e2[...] = <definition_of_the_equation>

model = Model(m, "dummy", equations=[e1,e2], problem=Problem.LP, sense=Sense.Max, objective=z)

client = NeosClient(
    email=os.environ["NEOS_EMAIL"],
    username=os.environ["NEOS_USER"],
    password=os.environ["NEOS_PASSWORD"],
)
model.solve(backend="neos", client=client)

Defining your username and password is optional for NEOS Server backend but it is recommended since it allows you to investigate your models on NEOS web client. The environment variables can be set in a .env file or with export statements in command line. Example to run your model on NEOS Server without authentication:

NEOS_EMAIL=<your_email> python <your_script>

If one wants to investigate the results later on NEOS Server web client, they can provide the username and password in the same way:

NEOS_EMAIL=<your_email> NEOS_USER=<your_username> NEOS_PASSWORD=<your_password> python <your_script>

Asynchronous Solve#

If you just want to send your jobs to NEOS server without blocking until the results are received, is_blocking parameter can be set to False in NeosClient.

All submitted jobs are stored in client.jobs in case you want to reach to the job numbers and job passwords you already sent to the server.

from gamspy import Container, Variable, Equation, Model, Sense, Problem, NeosClient
m = Container()
...
...
<define_your_model>
...
...
client = NeosClient(
    email=os.environ["NEOS_EMAIL"],
    username=os.environ["NEOS_USER"],
    password=os.environ["NEOS_PASSWORD"],
)

for _ in range(3):
    ...
    ...
    <changes_in_your_model>
    ...
    ...
    model.solve(backend="neos", client=client)

print(client.jobs) # This prints all job numbers and jon passwords as a list of tuples

The results of the non-blocking jobs can be retrieved later. For example if want to retrieve the results of the last submitted job, we can do that following:

job_number, job_password = client.jobs[-1]
client.get_final_results(job_number, job_password)
client.download_output(
    job_number, job_password, working_directory="my_out_directory"
)

The results would be downloaded to the given working directory. The downloaded gdx file will always have name “output.gdx”. Then, if one wants to read the results, they can simply create a new Container and read the results from the downloaded gdx file:

container = Container(load_from="my_out_directory/output.gdx")
# or
container = Container()
container.read("my_out_directory/output.gdx")

Terms of use for NEOS can be found here: Terms of use.

Solve Options#

Solve options can be specified as an gamspy.Options() class. For example:

from gamspy import Container, Variable, Equation, Model, Sense, Problem, Options

m = Container()

...
...
Definition of your model
...
...

model = Model(m, "my_model", equations=m.getEquations(), problem=Problem.LP, sense=Sense.Max, objective=z)
model.solve(options=Options(iteration_limit=2))

Here is the list of options and their descriptions:

Option

Description

Possible Values

cns

Default cns solver

Any solver installed in your system that can solve cns

dnlp

Default dnlp solver

Any solver installed in your system that can solve dnlp

emp

Default emp solver

Any solver installed in your system that can solve emp

lp

Default lp solver

Any solver installed in your system that can solve lp

mcp

Default mcp solver

Any solver installed in your system that can solve mcp

minlp

Default minlp solver

Any solver installed in your system that can solve minlp

mip

Default mip solver

Any solver installed in your system that can solve mip

miqcp

Default miqcp solver

Any solver installed in your system that can solve miqcp

mpec

Default mpec solver

Any solver installed in your system that can solve mpec

nlp

Default nlp solver

Any solver installed in your system that can solve nlp

qcp

Default qcp solver

Any solver installed in your system that can solve qcp

rminlp

Default rminlp solver

rmip

Default rmip solver

Any solver installed in your system that can solve rmip

rmiqcp

Default rmiqcp solver

rmpec

Default rmpec solver

Any solver installed in your system that can solve rmpec

allow_suffix_in_equation

Allow variables with suffixes in model algebra

bool

allow_suffix_in_limited_variables

Allow domain limited variables with suffixes in model

bool

basis_detection_threshold

Basis detection threshold

float

compile_error_limit

Compile time error limiy

int

domain_violation_limit

Domain violation limit solver default

int

gdx_file

Name of the gdx file with all symbols

str

job_time_limit

Elapsed time limit in seconds

float

job_heap_limit

Maximum Heap size allowed in MB

float

hold_fixed_variables

Treat fixed variables as constants

bool

integer_variable_upper_bound

Set mode for default upper bounds on integer variables

0: Set to +INF

1: Set to 100.

2: Set to 100 and write to the log if the level > 100

3: Same as 2 but issues an error if the level > 100

iteration_limit

Iteration limit of solver

int

keep_temporary_files

Controls keeping or deletion of process directory and scratch files

bool

license

Use alternative license file

Path to the alternative license

listing_file

Listing file name

Name of the listing file

log_file

Log file name

Name of the log file

variable_listing_limit

Maximum number of columns listed in one variable block

int

equation_listing_limit

Maximum number of rows listed in one equation block

int

node_limit

Node limit in branch and bound tree

int

absolute_optimality_gap

Absolute Optimality criterion solver default

float

relative_optimality_gap

Relative Optimality criterion solver default

float

profile

Execution profiling

0: No profiling

1: Minimum profiling

2: Profiling depth for nested control structures

profile_tolerance

Minimum time a statement must use to appear in profile generated output

float

time_limit

Wall-clock time limit for solver

float

savepoint

Save solver point in GDX file

0: No point GDX file is to be saved

1: A point GDX file from the last solve is to be saved

2: A point GDX file from every solve is to be saved

3: A point GDX file from the last solve is to be saved

4: A point GDX file from every solve is to be saved

seed

Random number seed

int

report_solution

Solution report print option

0: Remove solution listings following solves

1: Include solution listings following solves

2: Suppress all solution information

show_os_memory

0: Show memory reported by internal accounting

1: Show resident set size reported by operating system

2: Show virtual set size reported by operating system

solver_link_type

Solver link option

https://gams.com/45/docs/UG_GamsCall.html#GAMSAOsolvelink

multi_solve_strategy

Multiple solve management

“replace” | “merge” | “clear”

step_summary

Summary of computing resources used by job steps

bool

suppress_compiler_listing

Compiler listing option

bool

report_solver_status

Solver Status file reporting option

bool

threads

Number of threads to be used by a solver

int

trace_file

Trace file name

Name of the trace file

trace_level

Modelstat/Solvestat threshold used in conjunction with action=GT

int

trace_file_format

Trace file format option

0: Solver and GAMS step trace

1: Solver and GAMS exit trace

2: Solver trace only

3: Trace only in format used for GAMS performance world

5: Gams exit trace with all available trace fields

write_listing_file

Controls listing file creation

bool

zero_rounding_threshold

The results of certain operations will be set to zero if abs(result) LE ZeroRes

float

report_underflow

Report underflow as a warning when abs(results) LE ZeroRes and result set to zero

bool

Solver Options#

In addition to solve options, user can specify solver options to be used by the solver as a dictionary.:

from gamspy import Container, Variable, Equation, Model, Sense, Problem

m = Container()

...
...
Definition of your model
...
...

model = Model(m, "my_model", equations=m.getEquations(), problem=Problem.LP, sense=Sense.Max, objective=z)
model.solve(solver="CONOPT", solver_options=solver_options={"rtmaxv": "1.e12"})

For all possible solver options, please check the corresponding solver manual