Julia interface to SCIP solver.
See NEWS.md for changes in each (recent) release.
Update (August 2020)
On MacOS and Linux, it is no longer required to install the SCIP binaries using this package. There now exists a BinaryBuilder.jl generated package SCIP_jll.jl which is installed automatically as a dependency.
On Windows, the separate installation of SCIP is still mandatory.
Under Julia 1.3 or more recent, you can use this default installation:
pkg> add SCIP
If you use an older Julia version, Windows or want a custom SCIP installation, see below for the build steps.
Custom SCIP installations.
If you prefer to link to your own installation of SCIP, please set the
SCIPOPTDIR to point to the installation path. That
$SCIPOPTDIR/bin/scip.dll should exist, depending on your operating system.
When this is set before you install this package, it should be recognized automatically. Afterwards, you can trigger the build with
pkg> build SCIP
This step is also required if your Julia version is older than 1.3.
There are two ways of setting the parameters
(all are supported). First,
using MOI using SCIP optimizer = SCIP.Optimizer() MOI.set(optimizer, SCIP.Param("display/verblevel"), 0) MOI.set(optimizer, SCIP.Param("limits/gap"), 0.05)
Second, as keyword arguments to the constructor. But here, the slashes (
need to be replaced by underscores (
_) in order to end up with a valid Julia
identifier. This should not lead to ambiguities as none of the official SCIP
parameters contain any underscores (yet).
using MOI using SCIP optimizer = SCIP.Optimizer(display_verblevel=0, limits_gap=0.05)
Note that in both cases, the correct value type must be used (here,
Wrapper of Public API: All of SCIP's public API methods are wrapped and
available within the
SCIP package. This includes the
headers that are collected in
scip.h, as well as all default constraint
cons_*.h.) But the wrapped functions do not transform any data
structures and work on the raw pointers (e.g.
SCIP* in C,
Julia). Convenience wrapper functions based on Julia types are added as needed.
Memory Management: Programming with SCIP requires dealing with variable and
constraints objects that use reference
counting for memory management.
SCIP.jl provides a wrapper type
ManagedSCIP that collects lists of
SCIP_CONS* under the hood, and releases all reference when it is garbage
collected itself (via
finalize). When adding a variable (
add_variable) or a
add_linear_constraint), an integer index is returned. This index
can be used to retrieve the
SCIP_CONS* pointer via
ManagedSCIP does not currently support deletion of variables or constraints.
Supported Features for MathOptInterface: We aim at exposing many of SCIP's features through MathOptInterface. However, the focus is on keeping the wrapper simple and avoiding duplicate storage of model data.
As a consequence, we do not currently support some features such as retrieving
constraints by name (
SingleVariable-set constraints are not stored as SCIP
Support for more constraint types (quadratic/SOC, SOS1/2, nonlinear expression) is implemented, but SCIP itself only supports affine objective functions, so we will stick with that. More general objective functions could be implented via a bridge.
Supported operators in nonlinear expressions are as follows:
In particular, trigonometric functions are not supported.
Old Interface Implementation
Back then, the interface support MINLP problems as well as solver-indepentent callbacks for lazy constraints and heuristics.
To use that version, you need to pin the version of SCIP.jl to
last release before the rewrite):
pkg> add SCIP@v0.6.1 pkg> pin SCIP