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Sun, 14 Aug 2022 21:36:26 GMT (envelope-from git) Date: Sun, 14 Aug 2022 21:36:26 GMT Message-Id: <202208142136.27ELaQM4026830@gitrepo.freebsd.org> To: ports-committers@FreeBSD.org, dev-commits-ports-all@FreeBSD.org, dev-commits-ports-main@FreeBSD.org From: Yuri Victorovich Subject: git: ad61700fd52b - main - science/py-dwave-greedy: New port: DWave: Greedy binary quadratic model solvers List-Id: Commits to the main branch of the FreeBSD ports repository List-Archive: https://lists.freebsd.org/archives/dev-commits-ports-main List-Help: List-Post: List-Subscribe: List-Unsubscribe: Sender: owner-dev-commits-ports-main@freebsd.org X-BeenThere: dev-commits-ports-main@freebsd.org MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 8bit X-Git-Committer: yuri X-Git-Repository: ports X-Git-Refname: refs/heads/main X-Git-Reftype: branch X-Git-Commit: ad61700fd52b561895c3678513714ba5ca86ce13 Auto-Submitted: auto-generated ARC-Message-Signature: i=1; a=rsa-sha256; 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mx1.freebsd.org; none X-ThisMailContainsUnwantedMimeParts: N The branch main has been updated by yuri: URL: https://cgit.FreeBSD.org/ports/commit/?id=ad61700fd52b561895c3678513714ba5ca86ce13 commit ad61700fd52b561895c3678513714ba5ca86ce13 Author: Yuri Victorovich AuthorDate: 2022-08-14 21:35:38 +0000 Commit: Yuri Victorovich CommitDate: 2022-08-14 21:36:18 +0000 science/py-dwave-greedy: New port: DWave: Greedy binary quadratic model solvers --- science/Makefile | 1 + science/py-dwave-greedy/Makefile | 33 +++++++++++++++++++++++++++++++++ science/py-dwave-greedy/distinfo | 3 +++ science/py-dwave-greedy/pkg-descr | 9 +++++++++ 4 files changed, 46 insertions(+) diff --git a/science/Makefile b/science/Makefile index 6a3a53a85d18..365949ba8278 100644 --- a/science/Makefile +++ b/science/Makefile @@ -285,6 +285,7 @@ SUBDIR += py-dipy SUBDIR += py-dlib SUBDIR += py-dwave-cloud-client + SUBDIR += py-dwave-greedy SUBDIR += py-dwave-neal SUBDIR += py-dwave-networkx SUBDIR += py-dwave-preprocessing diff --git a/science/py-dwave-greedy/Makefile b/science/py-dwave-greedy/Makefile new file mode 100644 index 000000000000..931330ea1044 --- /dev/null +++ b/science/py-dwave-greedy/Makefile @@ -0,0 +1,33 @@ +PORTNAME= dwave-greedy +DISTVERSION= 0.2.5 +CATEGORIES= science python # quantum-computing +PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} + +MAINTAINER= yuri@FreeBSD.org +COMMENT= DWave: Greedy binary quadratic model solvers + +LICENSE= APACHE20 +LICENSE_FILE= ${WRKSRC}/LICENSE + +PY_DEPENDS= ${PYTHON_PKGNAMEPREFIX}dimod>0:science/py-dimod@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}importlib-metadata>=1.0:devel/py-importlib-metadata@${PY_FLAVOR} \ + ${PYNUMPY} +BUILD_DEPENDS= ${PY_DEPENDS} \ + ${PYTHON_PKGNAMEPREFIX}wheel>=0.30.0:devel/py-wheel@${PY_FLAVOR} +RUN_DEPENDS= ${PY_DEPENDS} +TEST_DEPENDS= ${PYTHON_PKGNAMEPREFIX}dwave-system>0:science/py-dwave-system@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}parameterized>0:devel/py-parameterized@${PY_FLAVOR} + +USES= python:3.7+ +USE_PYTHON= distutils cython autoplist unittest + +USE_GITHUB= yes +GH_ACCOUNT= dwavesystems + +TEST_ENV= ${MAKE_ENV} PYTHONPATH=${STAGEDIR}${PYTHONPREFIX_SITELIBDIR} +TEST_WRKSRC= ${WRKSRC}/tests + +post-install: + @${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/greedy/descent${PYTHON_EXT_SUFFIX}.so + +.include diff --git a/science/py-dwave-greedy/distinfo b/science/py-dwave-greedy/distinfo new file mode 100644 index 000000000000..7e0c8a1fff77 --- /dev/null +++ b/science/py-dwave-greedy/distinfo @@ -0,0 +1,3 @@ +TIMESTAMP = 1660466486 +SHA256 (dwavesystems-dwave-greedy-0.2.5_GH0.tar.gz) = 5b64041d1b6de713f11ea3c5c810ceebfb588c9cb9f8d687727fd9a8e85fcbeb +SIZE (dwavesystems-dwave-greedy-0.2.5_GH0.tar.gz) = 29039 diff --git a/science/py-dwave-greedy/pkg-descr b/science/py-dwave-greedy/pkg-descr new file mode 100644 index 000000000000..64bdfff93506 --- /dev/null +++ b/science/py-dwave-greedy/pkg-descr @@ -0,0 +1,9 @@ +dwave-greedy is an implementation of a steepest descent solver for binary +quadratic models. + +Steepest descent is the discrete analogue of gradient descent, but the best move +is computed using a local minimization rather rather than computing a gradient. +At each step, we determine the dimension along which to descend based on the +highest energy drop caused by a variable flip. + +WWW: https://docs.ocean.dwavesys.com/projects/greedy/en/latest/