Julia interface to SCIP solver.
See NEWS.md for changes in each (recent) release.
Update (March 2019)
We have completely rewritten the interface from scratch, using Clang.jl to generate wrappers based on the headers of the SCIP library. The goal is to support JuMP (from version 0.19 on) through MathOptInterface.
Currently, we support LP, MIP and QCP problems, as well as some nonlinear constraints, both through
(e.g., for second-order cones) as well as for expression graphs (see below).
It is now possible to implement SCIP constraint handlers in Julia. Other plugin types are not yet supported.
Currently, Linux, OS X and Windows are tested and supported.
We recommend using one of the provided installers, e.g.,
SCIPOptSuite-6.0.1-Linux.deb for systems based on Debian. Adding the SCIP.jl
package should then work out of the box:
pkg> add SCIP
If you build SCIP from source
you should set the environment variable
SCIPOPTDIR to point the the
installation path. That is, either
$SCIPOPTDIR/bin/scip.dll should exist,
depending on your operating system.
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