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<header>
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<h1 class="title">Module <code>miplearn.solvers.internal</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
|
|
|
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import logging
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional
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|
|
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from miplearn.instance import Instance
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from miplearn.types import (
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LPSolveStats,
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|
IterationCallback,
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|
LazyCallback,
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|
MIPSolveStats,
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|
VarIndex,
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|
Solution,
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|
BranchPriorities,
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|
Constraint,
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|
)
|
|
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logger = logging.getLogger(__name__)
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|
|
|
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class InternalSolver(ABC):
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|
"""
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|
Abstract class representing the MIP solver used internally by LearningSolver.
|
|
"""
|
|
|
|
@abstractmethod
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def solve_lp(
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self,
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tee: bool = False,
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|
) -> LPSolveStats:
|
|
"""
|
|
Solves the LP relaxation of the currently loaded instance. After this
|
|
method finishes, the solution can be retrieved by calling `get_solution`.
|
|
|
|
This method should not permanently modify the problem. That is, subsequent
|
|
calls to `solve` should solve the original MIP, not the LP relaxation.
|
|
|
|
Parameters
|
|
----------
|
|
tee
|
|
If true, prints the solver log to the screen.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
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|
def solve(
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|
self,
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|
tee: bool = False,
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|
iteration_cb: IterationCallback = None,
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|
lazy_cb: LazyCallback = None,
|
|
) -> MIPSolveStats:
|
|
"""
|
|
Solves the currently loaded instance. After this method finishes,
|
|
the best solution found can be retrieved by calling `get_solution`.
|
|
|
|
Parameters
|
|
----------
|
|
iteration_cb: IterationCallback
|
|
By default, InternalSolver makes a single call to the native `solve`
|
|
method and returns the result. If an iteration callback is provided
|
|
instead, InternalSolver enters a loop, where `solve` and `iteration_cb`
|
|
are called alternatively. To stop the loop, `iteration_cb` should return
|
|
False. Any other result causes the solver to loop again.
|
|
lazy_cb: LazyCallback
|
|
This function is called whenever the solver finds a new candidate
|
|
solution and can be used to add lazy constraints to the model. Only the
|
|
following operations within the callback are allowed:
|
|
- Querying the value of a variable
|
|
- Querying if a constraint is satisfied
|
|
- Adding a new constraint to the problem
|
|
Additional operations may be allowed by specific subclasses.
|
|
tee: bool
|
|
If true, prints the solver log to the screen.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_solution(self) -> Optional[Solution]:
|
|
"""
|
|
Returns current solution found by the solver.
|
|
|
|
If called after `solve`, returns the best primal solution found during
|
|
the search. If called after `solve_lp`, returns the optimal solution
|
|
to the LP relaxation. If no primal solution is available, return None.
|
|
|
|
The solution is a dictionary `sol`, where the optimal value of `var[idx]`
|
|
is given by `sol[var][idx]`.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def set_warm_start(self, solution: Solution) -> None:
|
|
"""
|
|
Sets the warm start to be used by the solver.
|
|
|
|
The solution should be a dictionary following the same format as the
|
|
one produced by `get_solution`. Only one warm start is supported.
|
|
Calling this function when a warm start already exists will
|
|
remove the previous warm start.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def set_instance(
|
|
self,
|
|
instance: Instance,
|
|
model: Any = None,
|
|
) -> None:
|
|
"""
|
|
Loads the given instance into the solver.
|
|
|
|
Parameters
|
|
----------
|
|
instance: Instance
|
|
The instance to be loaded.
|
|
model: Any
|
|
The concrete optimization model corresponding to this instance
|
|
(e.g. JuMP.Model or pyomo.core.ConcreteModel). If not provided,
|
|
it will be generated by calling `instance.to_model()`.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def fix(self, solution: Solution) -> None:
|
|
"""
|
|
Fixes the values of a subset of decision variables.
|
|
|
|
The values should be provided in the dictionary format generated by
|
|
`get_solution`. Missing values in the solution indicate variables
|
|
that should be left free.
|
|
"""
|
|
pass
|
|
|
|
def set_branching_priorities(self, priorities: BranchPriorities) -> None:
|
|
"""
|
|
Sets the branching priorities for the given decision variables.
|
|
|
|
When the MIP solver needs to decide on which variable to branch, variables
|
|
with higher priority are picked first, given that they are fractional.
|
|
Ties are solved arbitrarily. By default, all variables have priority zero.
|
|
|
|
The priorities should be provided in the dictionary format generated by
|
|
`get_solution`. Missing values indicate variables whose priorities
|
|
should not be modified.
|
|
"""
|
|
raise Exception("Not implemented")
|
|
|
|
@abstractmethod
|
|
def get_constraint_ids(self) -> List[str]:
|
|
"""
|
|
Returns a list of ids which uniquely identify each constraint in the model.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def add_constraint(self, cobj: Constraint) -> None:
|
|
"""
|
|
Adds a single constraint to the model.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def extract_constraint(self, cid: str) -> Constraint:
|
|
"""
|
|
Removes a given constraint from the model and returns an object `cobj` which
|
|
can be used to verify if the removed constraint is still satisfied by
|
|
the current solution, using `is_constraint_satisfied(cobj)`, and can potentially
|
|
be re-added to the model using `add_constraint(cobj)`.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def is_constraint_satisfied(self, cobj: Constraint) -> bool:
|
|
"""
|
|
Returns True if the current solution satisfies the given constraint.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def set_constraint_sense(self, cid: str, sense: str) -> None:
|
|
"""
|
|
Modifies the sense of a given constraint.
|
|
|
|
Parameters
|
|
----------
|
|
cid: str
|
|
The name of the constraint.
|
|
sense: str
|
|
The new sense (either "<", ">" or "=").
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_constraint_sense(self, cid: str) -> str:
|
|
"""
|
|
Returns the sense of a given constraint (either "<", ">" or "=").
|
|
|
|
Parameters
|
|
----------
|
|
cid: str
|
|
The name of the constraint.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_value(self, var_name: str, index: VarIndex) -> Optional[float]:
|
|
"""
|
|
Returns the value of a given variable in the current solution. If no
|
|
solution is available, returns None.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def relax(self) -> None:
|
|
"""
|
|
Drops all integrality constraints from the model.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_inequality_slacks(self) -> Dict[str, float]:
|
|
"""
|
|
Returns a dictionary mapping constraint name to the constraint slack
|
|
in the current solution.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def is_infeasible(self) -> bool:
|
|
"""
|
|
Returns True if the model has been proved to be infeasible.
|
|
Must be called after solve.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_dual(self, cid: str) -> float:
|
|
"""
|
|
If the model is feasible and has been solved to optimality, returns the
|
|
optimal value of the dual variable associated with this constraint. If the
|
|
model is infeasible, returns a portion of the infeasibility certificate
|
|
corresponding to the given constraint.
|
|
|
|
Only available for relaxed problems. Must be called after solve.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_sense(self) -> str:
|
|
"""
|
|
Returns the sense of the problem (either "min" or "max").
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_empty_solution(self) -> Dict:
|
|
"""
|
|
Returns a dictionary with the same shape as the one produced by
|
|
`get_solution`, but with all values set to None. This method is
|
|
used by the ML components to query what variables are there in
|
|
the model before a solution is available.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
<section>
|
|
<h2 class="section-title" id="header-classes">Classes</h2>
|
|
<dl>
|
|
<dt id="miplearn.solvers.internal.InternalSolver"><code class="flex name class">
|
|
<span>class <span class="ident">InternalSolver</span></span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Abstract class representing the MIP solver used internally by LearningSolver.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class InternalSolver(ABC):
|
|
"""
|
|
Abstract class representing the MIP solver used internally by LearningSolver.
|
|
"""
|
|
|
|
@abstractmethod
|
|
def solve_lp(
|
|
self,
|
|
tee: bool = False,
|
|
) -> LPSolveStats:
|
|
"""
|
|
Solves the LP relaxation of the currently loaded instance. After this
|
|
method finishes, the solution can be retrieved by calling `get_solution`.
|
|
|
|
This method should not permanently modify the problem. That is, subsequent
|
|
calls to `solve` should solve the original MIP, not the LP relaxation.
|
|
|
|
Parameters
|
|
----------
|
|
tee
|
|
If true, prints the solver log to the screen.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def solve(
|
|
self,
|
|
tee: bool = False,
|
|
iteration_cb: IterationCallback = None,
|
|
lazy_cb: LazyCallback = None,
|
|
) -> MIPSolveStats:
|
|
"""
|
|
Solves the currently loaded instance. After this method finishes,
|
|
the best solution found can be retrieved by calling `get_solution`.
|
|
|
|
Parameters
|
|
----------
|
|
iteration_cb: IterationCallback
|
|
By default, InternalSolver makes a single call to the native `solve`
|
|
method and returns the result. If an iteration callback is provided
|
|
instead, InternalSolver enters a loop, where `solve` and `iteration_cb`
|
|
are called alternatively. To stop the loop, `iteration_cb` should return
|
|
False. Any other result causes the solver to loop again.
|
|
lazy_cb: LazyCallback
|
|
This function is called whenever the solver finds a new candidate
|
|
solution and can be used to add lazy constraints to the model. Only the
|
|
following operations within the callback are allowed:
|
|
- Querying the value of a variable
|
|
- Querying if a constraint is satisfied
|
|
- Adding a new constraint to the problem
|
|
Additional operations may be allowed by specific subclasses.
|
|
tee: bool
|
|
If true, prints the solver log to the screen.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_solution(self) -> Optional[Solution]:
|
|
"""
|
|
Returns current solution found by the solver.
|
|
|
|
If called after `solve`, returns the best primal solution found during
|
|
the search. If called after `solve_lp`, returns the optimal solution
|
|
to the LP relaxation. If no primal solution is available, return None.
|
|
|
|
The solution is a dictionary `sol`, where the optimal value of `var[idx]`
|
|
is given by `sol[var][idx]`.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def set_warm_start(self, solution: Solution) -> None:
|
|
"""
|
|
Sets the warm start to be used by the solver.
|
|
|
|
The solution should be a dictionary following the same format as the
|
|
one produced by `get_solution`. Only one warm start is supported.
|
|
Calling this function when a warm start already exists will
|
|
remove the previous warm start.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def set_instance(
|
|
self,
|
|
instance: Instance,
|
|
model: Any = None,
|
|
) -> None:
|
|
"""
|
|
Loads the given instance into the solver.
|
|
|
|
Parameters
|
|
----------
|
|
instance: Instance
|
|
The instance to be loaded.
|
|
model: Any
|
|
The concrete optimization model corresponding to this instance
|
|
(e.g. JuMP.Model or pyomo.core.ConcreteModel). If not provided,
|
|
it will be generated by calling `instance.to_model()`.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def fix(self, solution: Solution) -> None:
|
|
"""
|
|
Fixes the values of a subset of decision variables.
|
|
|
|
The values should be provided in the dictionary format generated by
|
|
`get_solution`. Missing values in the solution indicate variables
|
|
that should be left free.
|
|
"""
|
|
pass
|
|
|
|
def set_branching_priorities(self, priorities: BranchPriorities) -> None:
|
|
"""
|
|
Sets the branching priorities for the given decision variables.
|
|
|
|
When the MIP solver needs to decide on which variable to branch, variables
|
|
with higher priority are picked first, given that they are fractional.
|
|
Ties are solved arbitrarily. By default, all variables have priority zero.
|
|
|
|
The priorities should be provided in the dictionary format generated by
|
|
`get_solution`. Missing values indicate variables whose priorities
|
|
should not be modified.
|
|
"""
|
|
raise Exception("Not implemented")
|
|
|
|
@abstractmethod
|
|
def get_constraint_ids(self) -> List[str]:
|
|
"""
|
|
Returns a list of ids which uniquely identify each constraint in the model.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def add_constraint(self, cobj: Constraint) -> None:
|
|
"""
|
|
Adds a single constraint to the model.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def extract_constraint(self, cid: str) -> Constraint:
|
|
"""
|
|
Removes a given constraint from the model and returns an object `cobj` which
|
|
can be used to verify if the removed constraint is still satisfied by
|
|
the current solution, using `is_constraint_satisfied(cobj)`, and can potentially
|
|
be re-added to the model using `add_constraint(cobj)`.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def is_constraint_satisfied(self, cobj: Constraint) -> bool:
|
|
"""
|
|
Returns True if the current solution satisfies the given constraint.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def set_constraint_sense(self, cid: str, sense: str) -> None:
|
|
"""
|
|
Modifies the sense of a given constraint.
|
|
|
|
Parameters
|
|
----------
|
|
cid: str
|
|
The name of the constraint.
|
|
sense: str
|
|
The new sense (either "<", ">" or "=").
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_constraint_sense(self, cid: str) -> str:
|
|
"""
|
|
Returns the sense of a given constraint (either "<", ">" or "=").
|
|
|
|
Parameters
|
|
----------
|
|
cid: str
|
|
The name of the constraint.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_value(self, var_name: str, index: VarIndex) -> Optional[float]:
|
|
"""
|
|
Returns the value of a given variable in the current solution. If no
|
|
solution is available, returns None.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def relax(self) -> None:
|
|
"""
|
|
Drops all integrality constraints from the model.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_inequality_slacks(self) -> Dict[str, float]:
|
|
"""
|
|
Returns a dictionary mapping constraint name to the constraint slack
|
|
in the current solution.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def is_infeasible(self) -> bool:
|
|
"""
|
|
Returns True if the model has been proved to be infeasible.
|
|
Must be called after solve.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_dual(self, cid: str) -> float:
|
|
"""
|
|
If the model is feasible and has been solved to optimality, returns the
|
|
optimal value of the dual variable associated with this constraint. If the
|
|
model is infeasible, returns a portion of the infeasibility certificate
|
|
corresponding to the given constraint.
|
|
|
|
Only available for relaxed problems. Must be called after solve.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_sense(self) -> str:
|
|
"""
|
|
Returns the sense of the problem (either "min" or "max").
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_empty_solution(self) -> Dict:
|
|
"""
|
|
Returns a dictionary with the same shape as the one produced by
|
|
`get_solution`, but with all values set to None. This method is
|
|
used by the ML components to query what variables are there in
|
|
the model before a solution is available.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
<h3>Ancestors</h3>
|
|
<ul class="hlist">
|
|
<li>abc.ABC</li>
|
|
</ul>
|
|
<h3>Subclasses</h3>
|
|
<ul class="hlist">
|
|
<li><a title="miplearn.solvers.gurobi.GurobiSolver" href="gurobi.html#miplearn.solvers.gurobi.GurobiSolver">GurobiSolver</a></li>
|
|
<li><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="pyomo/base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></li>
|
|
</ul>
|
|
<h3>Methods</h3>
|
|
<dl>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.add_constraint"><code class="name flex">
|
|
<span>def <span class="ident">add_constraint</span></span>(<span>self, cobj)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Adds a single constraint to the model.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def add_constraint(self, cobj: Constraint) -> None:
|
|
"""
|
|
Adds a single constraint to the model.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.extract_constraint"><code class="name flex">
|
|
<span>def <span class="ident">extract_constraint</span></span>(<span>self, cid)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Removes a given constraint from the model and returns an object <code>cobj</code> which
|
|
can be used to verify if the removed constraint is still satisfied by
|
|
the current solution, using <code>is_constraint_satisfied(cobj)</code>, and can potentially
|
|
be re-added to the model using <code>add_constraint(cobj)</code>.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def extract_constraint(self, cid: str) -> Constraint:
|
|
"""
|
|
Removes a given constraint from the model and returns an object `cobj` which
|
|
can be used to verify if the removed constraint is still satisfied by
|
|
the current solution, using `is_constraint_satisfied(cobj)`, and can potentially
|
|
be re-added to the model using `add_constraint(cobj)`.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.fix"><code class="name flex">
|
|
<span>def <span class="ident">fix</span></span>(<span>self, solution)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Fixes the values of a subset of decision variables.</p>
|
|
<p>The values should be provided in the dictionary format generated by
|
|
<code>get_solution</code>. Missing values in the solution indicate variables
|
|
that should be left free.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def fix(self, solution: Solution) -> None:
|
|
"""
|
|
Fixes the values of a subset of decision variables.
|
|
|
|
The values should be provided in the dictionary format generated by
|
|
`get_solution`. Missing values in the solution indicate variables
|
|
that should be left free.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.get_constraint_ids"><code class="name flex">
|
|
<span>def <span class="ident">get_constraint_ids</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns a list of ids which uniquely identify each constraint in the model.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def get_constraint_ids(self) -> List[str]:
|
|
"""
|
|
Returns a list of ids which uniquely identify each constraint in the model.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.get_constraint_sense"><code class="name flex">
|
|
<span>def <span class="ident">get_constraint_sense</span></span>(<span>self, cid)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns the sense of a given constraint (either "<", ">" or "=").</p>
|
|
<h2 id="parameters">Parameters</h2>
|
|
<dl>
|
|
<dt><strong><code>cid</code></strong> : <code>str</code></dt>
|
|
<dd>The name of the constraint.</dd>
|
|
</dl></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def get_constraint_sense(self, cid: str) -> str:
|
|
"""
|
|
Returns the sense of a given constraint (either "<", ">" or "=").
|
|
|
|
Parameters
|
|
----------
|
|
cid: str
|
|
The name of the constraint.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.get_dual"><code class="name flex">
|
|
<span>def <span class="ident">get_dual</span></span>(<span>self, cid)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>If the model is feasible and has been solved to optimality, returns the
|
|
optimal value of the dual variable associated with this constraint. If the
|
|
model is infeasible, returns a portion of the infeasibility certificate
|
|
corresponding to the given constraint.</p>
|
|
<p>Only available for relaxed problems. Must be called after solve.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def get_dual(self, cid: str) -> float:
|
|
"""
|
|
If the model is feasible and has been solved to optimality, returns the
|
|
optimal value of the dual variable associated with this constraint. If the
|
|
model is infeasible, returns a portion of the infeasibility certificate
|
|
corresponding to the given constraint.
|
|
|
|
Only available for relaxed problems. Must be called after solve.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.get_empty_solution"><code class="name flex">
|
|
<span>def <span class="ident">get_empty_solution</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns a dictionary with the same shape as the one produced by
|
|
<code>get_solution</code>, but with all values set to None. This method is
|
|
used by the ML components to query what variables are there in
|
|
the model before a solution is available.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def get_empty_solution(self) -> Dict:
|
|
"""
|
|
Returns a dictionary with the same shape as the one produced by
|
|
`get_solution`, but with all values set to None. This method is
|
|
used by the ML components to query what variables are there in
|
|
the model before a solution is available.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.get_inequality_slacks"><code class="name flex">
|
|
<span>def <span class="ident">get_inequality_slacks</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns a dictionary mapping constraint name to the constraint slack
|
|
in the current solution.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def get_inequality_slacks(self) -> Dict[str, float]:
|
|
"""
|
|
Returns a dictionary mapping constraint name to the constraint slack
|
|
in the current solution.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.get_sense"><code class="name flex">
|
|
<span>def <span class="ident">get_sense</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns the sense of the problem (either "min" or "max").</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def get_sense(self) -> str:
|
|
"""
|
|
Returns the sense of the problem (either "min" or "max").
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.get_solution"><code class="name flex">
|
|
<span>def <span class="ident">get_solution</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns current solution found by the solver.</p>
|
|
<p>If called after <code>solve</code>, returns the best primal solution found during
|
|
the search. If called after <code>solve_lp</code>, returns the optimal solution
|
|
to the LP relaxation. If no primal solution is available, return None.</p>
|
|
<p>The solution is a dictionary <code>sol</code>, where the optimal value of <code>var[idx]</code>
|
|
is given by <code>sol[var][idx]</code>.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def get_solution(self) -> Optional[Solution]:
|
|
"""
|
|
Returns current solution found by the solver.
|
|
|
|
If called after `solve`, returns the best primal solution found during
|
|
the search. If called after `solve_lp`, returns the optimal solution
|
|
to the LP relaxation. If no primal solution is available, return None.
|
|
|
|
The solution is a dictionary `sol`, where the optimal value of `var[idx]`
|
|
is given by `sol[var][idx]`.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.get_value"><code class="name flex">
|
|
<span>def <span class="ident">get_value</span></span>(<span>self, var_name, index)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns the value of a given variable in the current solution. If no
|
|
solution is available, returns None.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def get_value(self, var_name: str, index: VarIndex) -> Optional[float]:
|
|
"""
|
|
Returns the value of a given variable in the current solution. If no
|
|
solution is available, returns None.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.is_constraint_satisfied"><code class="name flex">
|
|
<span>def <span class="ident">is_constraint_satisfied</span></span>(<span>self, cobj)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns True if the current solution satisfies the given constraint.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def is_constraint_satisfied(self, cobj: Constraint) -> bool:
|
|
"""
|
|
Returns True if the current solution satisfies the given constraint.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.is_infeasible"><code class="name flex">
|
|
<span>def <span class="ident">is_infeasible</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns True if the model has been proved to be infeasible.
|
|
Must be called after solve.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def is_infeasible(self) -> bool:
|
|
"""
|
|
Returns True if the model has been proved to be infeasible.
|
|
Must be called after solve.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.relax"><code class="name flex">
|
|
<span>def <span class="ident">relax</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Drops all integrality constraints from the model.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def relax(self) -> None:
|
|
"""
|
|
Drops all integrality constraints from the model.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.set_branching_priorities"><code class="name flex">
|
|
<span>def <span class="ident">set_branching_priorities</span></span>(<span>self, priorities)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Sets the branching priorities for the given decision variables.</p>
|
|
<p>When the MIP solver needs to decide on which variable to branch, variables
|
|
with higher priority are picked first, given that they are fractional.
|
|
Ties are solved arbitrarily. By default, all variables have priority zero.</p>
|
|
<p>The priorities should be provided in the dictionary format generated by
|
|
<code>get_solution</code>. Missing values indicate variables whose priorities
|
|
should not be modified.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def set_branching_priorities(self, priorities: BranchPriorities) -> None:
|
|
"""
|
|
Sets the branching priorities for the given decision variables.
|
|
|
|
When the MIP solver needs to decide on which variable to branch, variables
|
|
with higher priority are picked first, given that they are fractional.
|
|
Ties are solved arbitrarily. By default, all variables have priority zero.
|
|
|
|
The priorities should be provided in the dictionary format generated by
|
|
`get_solution`. Missing values indicate variables whose priorities
|
|
should not be modified.
|
|
"""
|
|
raise Exception("Not implemented")</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.set_constraint_sense"><code class="name flex">
|
|
<span>def <span class="ident">set_constraint_sense</span></span>(<span>self, cid, sense)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Modifies the sense of a given constraint.</p>
|
|
<h2 id="parameters">Parameters</h2>
|
|
<dl>
|
|
<dt><strong><code>cid</code></strong> : <code>str</code></dt>
|
|
<dd>The name of the constraint.</dd>
|
|
<dt><strong><code>sense</code></strong> : <code>str</code></dt>
|
|
<dd>The new sense (either "<", ">" or "=").</dd>
|
|
</dl></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def set_constraint_sense(self, cid: str, sense: str) -> None:
|
|
"""
|
|
Modifies the sense of a given constraint.
|
|
|
|
Parameters
|
|
----------
|
|
cid: str
|
|
The name of the constraint.
|
|
sense: str
|
|
The new sense (either "<", ">" or "=").
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.set_instance"><code class="name flex">
|
|
<span>def <span class="ident">set_instance</span></span>(<span>self, instance, model=None)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Loads the given instance into the solver.</p>
|
|
<h2 id="parameters">Parameters</h2>
|
|
<dl>
|
|
<dt><strong><code>instance</code></strong> : <code>Instance</code></dt>
|
|
<dd>The instance to be loaded.</dd>
|
|
<dt><strong><code>model</code></strong> : <code>Any</code></dt>
|
|
<dd>The concrete optimization model corresponding to this instance
|
|
(e.g. JuMP.Model or pyomo.core.ConcreteModel). If not provided,
|
|
it will be generated by calling <code>instance.to_model()</code>.</dd>
|
|
</dl></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def set_instance(
|
|
self,
|
|
instance: Instance,
|
|
model: Any = None,
|
|
) -> None:
|
|
"""
|
|
Loads the given instance into the solver.
|
|
|
|
Parameters
|
|
----------
|
|
instance: Instance
|
|
The instance to be loaded.
|
|
model: Any
|
|
The concrete optimization model corresponding to this instance
|
|
(e.g. JuMP.Model or pyomo.core.ConcreteModel). If not provided,
|
|
it will be generated by calling `instance.to_model()`.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.set_warm_start"><code class="name flex">
|
|
<span>def <span class="ident">set_warm_start</span></span>(<span>self, solution)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Sets the warm start to be used by the solver.</p>
|
|
<p>The solution should be a dictionary following the same format as the
|
|
one produced by <code>get_solution</code>. Only one warm start is supported.
|
|
Calling this function when a warm start already exists will
|
|
remove the previous warm start.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def set_warm_start(self, solution: Solution) -> None:
|
|
"""
|
|
Sets the warm start to be used by the solver.
|
|
|
|
The solution should be a dictionary following the same format as the
|
|
one produced by `get_solution`. Only one warm start is supported.
|
|
Calling this function when a warm start already exists will
|
|
remove the previous warm start.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.solve"><code class="name flex">
|
|
<span>def <span class="ident">solve</span></span>(<span>self, tee=False, iteration_cb=None, lazy_cb=None)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Solves the currently loaded instance. After this method finishes,
|
|
the best solution found can be retrieved by calling <code>get_solution</code>.</p>
|
|
<h2 id="parameters">Parameters</h2>
|
|
<dl>
|
|
<dt><strong><code>iteration_cb</code></strong> : <code>IterationCallback</code></dt>
|
|
<dd>By default, InternalSolver makes a single call to the native <code>solve</code>
|
|
method and returns the result. If an iteration callback is provided
|
|
instead, InternalSolver enters a loop, where <code>solve</code> and <code>iteration_cb</code>
|
|
are called alternatively. To stop the loop, <code>iteration_cb</code> should return
|
|
False. Any other result causes the solver to loop again.</dd>
|
|
<dt><strong><code>lazy_cb</code></strong> : <code>LazyCallback</code></dt>
|
|
<dd>This function is called whenever the solver finds a new candidate
|
|
solution and can be used to add lazy constraints to the model. Only the
|
|
following operations within the callback are allowed:
|
|
- Querying the value of a variable
|
|
- Querying if a constraint is satisfied
|
|
- Adding a new constraint to the problem
|
|
Additional operations may be allowed by specific subclasses.</dd>
|
|
<dt><strong><code>tee</code></strong> : <code>bool</code></dt>
|
|
<dd>If true, prints the solver log to the screen.</dd>
|
|
</dl></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def solve(
|
|
self,
|
|
tee: bool = False,
|
|
iteration_cb: IterationCallback = None,
|
|
lazy_cb: LazyCallback = None,
|
|
) -> MIPSolveStats:
|
|
"""
|
|
Solves the currently loaded instance. After this method finishes,
|
|
the best solution found can be retrieved by calling `get_solution`.
|
|
|
|
Parameters
|
|
----------
|
|
iteration_cb: IterationCallback
|
|
By default, InternalSolver makes a single call to the native `solve`
|
|
method and returns the result. If an iteration callback is provided
|
|
instead, InternalSolver enters a loop, where `solve` and `iteration_cb`
|
|
are called alternatively. To stop the loop, `iteration_cb` should return
|
|
False. Any other result causes the solver to loop again.
|
|
lazy_cb: LazyCallback
|
|
This function is called whenever the solver finds a new candidate
|
|
solution and can be used to add lazy constraints to the model. Only the
|
|
following operations within the callback are allowed:
|
|
- Querying the value of a variable
|
|
- Querying if a constraint is satisfied
|
|
- Adding a new constraint to the problem
|
|
Additional operations may be allowed by specific subclasses.
|
|
tee: bool
|
|
If true, prints the solver log to the screen.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.internal.InternalSolver.solve_lp"><code class="name flex">
|
|
<span>def <span class="ident">solve_lp</span></span>(<span>self, tee=False)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Solves the LP relaxation of the currently loaded instance. After this
|
|
method finishes, the solution can be retrieved by calling <code>get_solution</code>.</p>
|
|
<p>This method should not permanently modify the problem. That is, subsequent
|
|
calls to <code>solve</code> should solve the original MIP, not the LP relaxation.</p>
|
|
<h2 id="parameters">Parameters</h2>
|
|
<dl>
|
|
<dt><strong><code>tee</code></strong></dt>
|
|
<dd>If true, prints the solver log to the screen.</dd>
|
|
</dl></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def solve_lp(
|
|
self,
|
|
tee: bool = False,
|
|
) -> LPSolveStats:
|
|
"""
|
|
Solves the LP relaxation of the currently loaded instance. After this
|
|
method finishes, the solution can be retrieved by calling `get_solution`.
|
|
|
|
This method should not permanently modify the problem. That is, subsequent
|
|
calls to `solve` should solve the original MIP, not the LP relaxation.
|
|
|
|
Parameters
|
|
----------
|
|
tee
|
|
If true, prints the solver log to the screen.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</dd>
|
|
</dl>
|
|
</section>
|
|
</article>
|
|
<nav id="sidebar">
|
|
<h1>Index</h1>
|
|
<div class="toc">
|
|
<ul></ul>
|
|
</div>
|
|
<ul id="index">
|
|
<li><h3>Super-module</h3>
|
|
<ul>
|
|
<li><code><a title="miplearn.solvers" href="index.html">miplearn.solvers</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li><h3><a href="#header-classes">Classes</a></h3>
|
|
<ul>
|
|
<li>
|
|
<h4><code><a title="miplearn.solvers.internal.InternalSolver" href="#miplearn.solvers.internal.InternalSolver">InternalSolver</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.add_constraint" href="#miplearn.solvers.internal.InternalSolver.add_constraint">add_constraint</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.extract_constraint" href="#miplearn.solvers.internal.InternalSolver.extract_constraint">extract_constraint</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.fix" href="#miplearn.solvers.internal.InternalSolver.fix">fix</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_constraint_ids" href="#miplearn.solvers.internal.InternalSolver.get_constraint_ids">get_constraint_ids</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_constraint_sense" href="#miplearn.solvers.internal.InternalSolver.get_constraint_sense">get_constraint_sense</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_dual" href="#miplearn.solvers.internal.InternalSolver.get_dual">get_dual</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_empty_solution" href="#miplearn.solvers.internal.InternalSolver.get_empty_solution">get_empty_solution</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_inequality_slacks" href="#miplearn.solvers.internal.InternalSolver.get_inequality_slacks">get_inequality_slacks</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_sense" href="#miplearn.solvers.internal.InternalSolver.get_sense">get_sense</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_solution" href="#miplearn.solvers.internal.InternalSolver.get_solution">get_solution</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_value" href="#miplearn.solvers.internal.InternalSolver.get_value">get_value</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.is_constraint_satisfied" href="#miplearn.solvers.internal.InternalSolver.is_constraint_satisfied">is_constraint_satisfied</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.is_infeasible" href="#miplearn.solvers.internal.InternalSolver.is_infeasible">is_infeasible</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.relax" href="#miplearn.solvers.internal.InternalSolver.relax">relax</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_branching_priorities" href="#miplearn.solvers.internal.InternalSolver.set_branching_priorities">set_branching_priorities</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_constraint_sense" href="#miplearn.solvers.internal.InternalSolver.set_constraint_sense">set_constraint_sense</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_instance" href="#miplearn.solvers.internal.InternalSolver.set_instance">set_instance</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_warm_start" href="#miplearn.solvers.internal.InternalSolver.set_warm_start">set_warm_start</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.solve" href="#miplearn.solvers.internal.InternalSolver.solve">solve</a></code></li>
|
|
<li><code><a title="miplearn.solvers.internal.InternalSolver.solve_lp" href="#miplearn.solvers.internal.InternalSolver.solve_lp">solve_lp</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
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