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83669676 diff --git a/30_ieee/train-00118-of-00119.parquet b/30_ieee/train-00118-of-00119.parquet new file mode 100644 index 0000000000000000000000000000000000000000..65059ab23568ecde8b07414eef3f6de578f12811 --- /dev/null +++ b/30_ieee/train-00118-of-00119.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b37c14829e767649f6ed220136bd1db196d97b8a72eaa84c4e6fa0b0aedf2bc3 +size 83668277 diff --git a/PGLearn-Small-30_ieee.py b/PGLearn-Small-30_ieee.py deleted file mode 100644 index 1f04e95686b7b4833cdcd11c1e6aefa7cd66f82d..0000000000000000000000000000000000000000 --- a/PGLearn-Small-30_ieee.py +++ /dev/null @@ -1,397 +0,0 @@ -from __future__ import annotations -from dataclasses import dataclass -from pathlib import Path -import json -import gzip - -import datasets as hfd -import h5py -import pyarrow as pa - -# ┌──────────────┐ -# │ Metadata │ -# └──────────────┘ - -@dataclass -class CaseSizes: - n_bus: int - n_load: int - n_gen: int - n_branch: int - -CASENAME = "30_ieee" -SIZES = CaseSizes(n_bus=30, n_load=21, n_gen=6, n_branch=41) -NUM_TRAIN = 793067 -NUM_TEST = 198267 -NUM_INFEASIBLE = 8666 - -URL = "https://huggingface.co/datasets/PGLearn/PGLearn-Small-30_ieee" -DESCRIPTION = """\ -The 30_ieee PGLearn optimal power flow dataset, part of the PGLearn-Small collection. \ -""" -VERSION = hfd.Version("1.0.0") -DEFAULT_CONFIG_DESCRIPTION="""\ -This configuration contains feasible input, metadata, primal solution, and dual solution data \ -for the ACOPF, DCOPF, and SOCOPF formulations on the {case} system. -""" -USE_ML4OPF_WARNING = """ -================================================================================================ - Loading PGLearn-Small-30_ieee through the `datasets.load_dataset` function may be slow. - - Consider using ML4OPF to directly convert to `torch.Tensor`; for more info see: - https://github.com/AI4OPT/ML4OPF?tab=readme-ov-file#manually-loading-data - - Or, use `huggingface_hub.snapshot_download` and an HDF5 reader; for more info see: - https://huggingface.co/datasets/PGLearn/PGLearn-Small-30_ieee#downloading-individual-files -================================================================================================ -""" -CITATION = """\ -@article{klamkinpglearn, - title={{PGLearn - An Open-Source Learning Toolkit for Optimal Power Flow}}, - author={Klamkin, Michael and Tanneau, Mathieu and Van Hentenryck, Pascal}, - year={2025}, -}\ -""" - -IS_COMPRESSED = True - -# ┌──────────────────┐ -# │ Formulations │ -# └──────────────────┘ - -def acopf_features(sizes: CaseSizes, primal: bool, dual: bool, meta: bool): - features = {} - if primal: features.update(acopf_primal_features(sizes)) - if dual: features.update(acopf_dual_features(sizes)) - if meta: features.update({f"ACOPF/{k}": v for k, v in META_FEATURES.items()}) - return features - -def dcopf_features(sizes: CaseSizes, primal: bool, dual: bool, meta: bool): - features = {} - if primal: features.update(dcopf_primal_features(sizes)) - if dual: features.update(dcopf_dual_features(sizes)) - if meta: features.update({f"DCOPF/{k}": v for k, v in META_FEATURES.items()}) - return features - -def socopf_features(sizes: CaseSizes, primal: bool, dual: bool, meta: bool): - features = {} - if primal: features.update(socopf_primal_features(sizes)) - if dual: features.update(socopf_dual_features(sizes)) - if meta: features.update({f"SOCOPF/{k}": v for k, v in META_FEATURES.items()}) - return features - -FORMULATIONS_TO_FEATURES = { - "ACOPF": acopf_features, - "DCOPF": dcopf_features, - "SOCOPF": socopf_features, -} - -# ┌───────────────────┐ -# │ BuilderConfig │ -# └───────────────────┘ - -class PGLearnSmall30_ieeeConfig(hfd.BuilderConfig): - """BuilderConfig for PGLearn-Small-30_ieee. - By default, primal solution data, metadata, input, casejson, are included for the train and test splits. - - To modify the default configuration, pass attributes of this class to `datasets.load_dataset`: - - Attributes: - formulations (list[str]): The formulation(s) to include, e.g. ["ACOPF", "DCOPF"] - primal (bool, optional): Include primal solution data. Defaults to True. - dual (bool, optional): Include dual solution data. Defaults to False. - meta (bool, optional): Include metadata. Defaults to True. - input (bool, optional): Include input data. Defaults to True. - casejson (bool, optional): Include case.json data. Defaults to True. - train (bool, optional): Include training samples. Defaults to True. - test (bool, optional): Include testing samples. Defaults to True. - infeasible (bool, optional): Include infeasible samples. Defaults to False. - """ - def __init__(self, - formulations: list[str], - primal: bool=True, dual: bool=False, meta: bool=True, input: bool = True, casejson: bool=True, - train: bool=True, test: bool=True, infeasible: bool=False, - compressed: bool=IS_COMPRESSED, **kwargs - ): - super(PGLearnSmall30_ieeeConfig, self).__init__(version=VERSION, **kwargs) - - self.case = CASENAME - self.formulations = formulations - - self.primal = primal - self.dual = dual - self.meta = meta - self.input = input - self.casejson = casejson - - self.train = train - self.test = test - self.infeasible = infeasible - - self.gz_ext = ".gz" if compressed else "" - - @property - def size(self): - return SIZES - - @property - def features(self): - features = {} - if self.casejson: features.update(case_features()) - if self.input: features.update(input_features(SIZES)) - for formulation in self.formulations: - features.update(FORMULATIONS_TO_FEATURES[formulation](SIZES, self.primal, self.dual, self.meta)) - return hfd.Features(features) - - @property - def splits(self): - splits: dict[hfd.Split, dict[str, str | int]] = {} - if self.train: - splits[hfd.Split.TRAIN] = { - "name": "train", - "num_examples": NUM_TRAIN - } - if self.test: - splits[hfd.Split.TEST] = { - "name": "test", - "num_examples": NUM_TEST - } - if self.infeasible: - splits[hfd.Split("infeasible")] = { - "name": "infeasible", - "num_examples": NUM_INFEASIBLE - } - return splits - - @property - def urls(self): - urls: dict[str, None | str | list] = { - "case": None, "train": [], "test": [], "infeasible": [], - } - - if self.casejson: urls["case"] = f"case.json" + self.gz_ext - - split_names = [] - if self.train: split_names.append("train") - if self.test: split_names.append("test") - if self.infeasible: split_names.append("infeasible") - - for split in split_names: - if self.input: urls[split].append(f"{split}/input.h5" + self.gz_ext) - for formulation in self.formulations: - if self.primal: urls[split].append(f"{split}/{formulation}/primal.h5" + self.gz_ext) - if self.dual: urls[split].append(f"{split}/{formulation}/dual.h5" + self.gz_ext) - if self.meta: urls[split].append(f"{split}/{formulation}/meta.h5" + self.gz_ext) - return urls - -# ┌────────────────────┐ -# │ DatasetBuilder │ -# └────────────────────┘ - -class PGLearnSmall30_ieee(hfd.ArrowBasedBuilder): - """DatasetBuilder for PGLearn-Small-30_ieee. - The main interface is `datasets.load_dataset` with `trust_remote_code=True`, e.g. - - ```python - from datasets import load_dataset - ds = load_dataset("PGLearn/PGLearn-Small-30_ieee", trust_remote_code=True, - # modify the default configuration by passing kwargs - formulations=["DCOPF"], - dual=False, - meta=False, - ) - ``` - """ - - DEFAULT_WRITER_BATCH_SIZE = 10000 - BUILDER_CONFIG_CLASS = PGLearnSmall30_ieeeConfig - DEFAULT_CONFIG_NAME=CASENAME - BUILDER_CONFIGS = [ - PGLearnSmall30_ieeeConfig( - name=CASENAME, description=DEFAULT_CONFIG_DESCRIPTION.format(case=CASENAME), - formulations=list(FORMULATIONS_TO_FEATURES.keys()), - primal=True, dual=True, meta=True, input=True, casejson=True, - train=True, test=True, infeasible=False, - ) - ] - - def _info(self): - return hfd.DatasetInfo( - features=self.config.features, splits=self.config.splits, - description=DESCRIPTION + self.config.description, - homepage=URL, citation=CITATION, - ) - - def _split_generators(self, dl_manager: hfd.DownloadManager): - hfd.logging.get_logger().warning(USE_ML4OPF_WARNING) - - filepaths = dl_manager.download_and_extract(self.config.urls) - - splits: list[hfd.SplitGenerator] = [] - if self.config.train: - splits.append(hfd.SplitGenerator( - name=hfd.Split.TRAIN, - gen_kwargs=dict(case_file=filepaths["case"], data_files=tuple(filepaths["train"]), n_samples=NUM_TRAIN), - )) - if self.config.test: - splits.append(hfd.SplitGenerator( - name=hfd.Split.TEST, - gen_kwargs=dict(case_file=filepaths["case"], data_files=tuple(filepaths["test"]), n_samples=NUM_TEST), - )) - if self.config.infeasible: - splits.append(hfd.SplitGenerator( - name=hfd.Split("infeasible"), - gen_kwargs=dict(case_file=filepaths["case"], data_files=tuple(filepaths["infeasible"]), n_samples=NUM_INFEASIBLE), - )) - return splits - - def _generate_tables(self, case_file: str | None, data_files: tuple[hfd.utils.track.tracked_str], n_samples: int): - case_data: str | None = json.dumps(json.load(open_maybe_gzip(case_file))) if case_file is not None else None - - opened_files = [open_maybe_gzip(file) for file in data_files] - data = {'/'.join(Path(df.get_origin()).parts[-2:]).split('.')[0]: h5py.File(of) for of, df in zip(opened_files, data_files)} - for k in list(data.keys()): - if "/input" in k: data[k.split("/", 1)[1]] = data.pop(k) - - batch_size = self._writer_batch_size or self.DEFAULT_WRITER_BATCH_SIZE - for i in range(0, n_samples, batch_size): - effective_batch_size = min(batch_size, n_samples - i) - - sample_data = { - f"{dk}/{k}": - hfd.features.features.numpy_to_pyarrow_listarray(v[i:i + effective_batch_size, ...]) - for dk, d in data.items() for k, v in d.items() if f"{dk}/{k}" in self.config.features - } - - if case_data is not None: - sample_data["case/json"] = pa.array([case_data] * effective_batch_size) - - yield i, pa.Table.from_pydict(sample_data) - - for f in opened_files: - f.close() - -# ┌──────────────┐ -# │ Features │ -# └──────────────┘ - -FLOAT_TYPE = "float32" -INT_TYPE = "int64" -BOOL_TYPE = "bool" -STRING_TYPE = "string" - -def case_features(): - # FIXME: better way to share schema of case data -- need to treat jagged arrays - return { - "case/json": hfd.Value(STRING_TYPE), - } - -META_FEATURES = { - "meta/seed": hfd.Value(dtype=INT_TYPE), - "meta/formulation": hfd.Value(dtype=STRING_TYPE), - "meta/primal_objective_value": hfd.Value(dtype=FLOAT_TYPE), - "meta/dual_objective_value": hfd.Value(dtype=FLOAT_TYPE), - "meta/primal_status": hfd.Value(dtype=STRING_TYPE), - "meta/dual_status": hfd.Value(dtype=STRING_TYPE), - "meta/termination_status": hfd.Value(dtype=STRING_TYPE), - "meta/build_time": hfd.Value(dtype=FLOAT_TYPE), - "meta/extract_time": hfd.Value(dtype=FLOAT_TYPE), - "meta/solve_time": hfd.Value(dtype=FLOAT_TYPE), -} - -def input_features(sizes: CaseSizes): - return { - "input/pd": hfd.Sequence(length=sizes.n_load, feature=hfd.Value(dtype=FLOAT_TYPE)), - "input/qd": hfd.Sequence(length=sizes.n_load, feature=hfd.Value(dtype=FLOAT_TYPE)), - "input/gen_status": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=BOOL_TYPE)), - "input/branch_status": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=BOOL_TYPE)), - "input/seed": hfd.Value(dtype=INT_TYPE), - } - -def acopf_primal_features(sizes: CaseSizes): - return { - "ACOPF/primal/vm": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/va": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - } -def acopf_dual_features(sizes: CaseSizes): - return { - "ACOPF/dual/kcl_p": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/kcl_q": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/vm": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/ohm_pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/ohm_pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/ohm_qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/ohm_qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/va_diff": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/sm_fr": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/sm_to": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/slack_bus": hfd.Value(dtype=FLOAT_TYPE), - } -def dcopf_primal_features(sizes: CaseSizes): - return { - "DCOPF/primal/va": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/primal/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/primal/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - } -def dcopf_dual_features(sizes: CaseSizes): - return { - "DCOPF/dual/kcl_p": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/dual/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/dual/ohm_pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/dual/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/dual/va_diff": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/dual/slack_bus": hfd.Value(dtype=FLOAT_TYPE), - } -def socopf_primal_features(sizes: CaseSizes): - return { - "SOCOPF/primal/w": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/wr": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/wi": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - } -def socopf_dual_features(sizes: CaseSizes): - return { - "SOCOPF/dual/kcl_p": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/kcl_q": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/w": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/ohm_pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/ohm_pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/ohm_qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/ohm_qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/jabr": hfd.Array2D(shape=(sizes.n_branch, 4), dtype=FLOAT_TYPE), - "SOCOPF/dual/sm_fr": hfd.Array2D(shape=(sizes.n_branch, 3), dtype=FLOAT_TYPE), - "SOCOPF/dual/sm_to": hfd.Array2D(shape=(sizes.n_branch, 3), dtype=FLOAT_TYPE), - "SOCOPF/dual/va_diff": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/wr": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/wi": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - } - -# ┌───────────────┐ -# │ Utilities │ -# └───────────────┘ - -def open_maybe_gzip(path): - return gzip.open(path, "rb") if path.endswith(".gz") else open(path, "rb") diff --git a/README.md b/README.md index 105a58f165f0bd16803937dfbb3afc192eb8ce78..cfd06634f78ce933f6c2a38d102ab10717188aac 100644 --- a/README.md +++ b/README.md @@ -290,6 +290,14 @@ dataset_info: - name: test num_bytes: 14791486486 num_examples: 198267 - download_size: 7531690754 + download_size: 12445672526 dataset_size: 73957357822 +configs: +- config_name: 30_ieee + data_files: + - split: train + path: 30_ieee/train-* + - split: test + path: 30_ieee/test-* + default: true --- diff --git a/case.json.gz b/case.json.gz deleted file mode 100644 index 28a5b78e0ae00ec4507e92c75f214a8bcee2b3e1..0000000000000000000000000000000000000000 --- a/case.json.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c284d9a6a0ca63298f98cc8f53892f3f0253b72a8bb181f344c9db88f7a496ee -size 24607 diff --git a/config.toml b/config.toml deleted file mode 100644 index 53f26b7187f520b9233af12c6a0341cfbb7c0e60..0000000000000000000000000000000000000000 --- a/config.toml +++ /dev/null @@ -1,42 +0,0 @@ -# Name of the reference PGLib case. Must be a valid PGLib case name. -pglib_case = "pglib_opf_case30_ieee" -floating_point_type = "Float32" - -[sampler] -# data sampler options -[sampler.load] -noise_type = "ScaledUniform" -l = 0.6 # Lower bound of base load factor -u = 1.0 # Upper bound of base load factor -sigma = 0.20 # Relative (multiplicative) noise level. - - -[OPF] - -[OPF.ACOPF] -type = "ACOPF" -solver.name = "Ipopt" -solver.attributes.tol = 1e-6 -solver.attributes.linear_solver = "ma27" - -[OPF.DCOPF] -# Formulation/solver options -type = "DCOPF" -solver.name = "HiGHS" - -[OPF.SOCOPF] -type = "SOCOPF" -solver.name = "Clarabel" -# Tight tolerances -solver.attributes.tol_gap_abs = 1e-6 -solver.attributes.tol_gap_rel = 1e-6 -solver.attributes.tol_feas = 1e-6 -solver.attributes.tol_infeas_rel = 1e-6 -solver.attributes.tol_ktratio = 1e-6 -# Reduced accuracy settings -solver.attributes.reduced_tol_gap_abs = 1e-6 -solver.attributes.reduced_tol_gap_rel = 1e-6 -solver.attributes.reduced_tol_feas = 1e-6 -solver.attributes.reduced_tol_infeas_abs = 1e-6 -solver.attributes.reduced_tol_infeas_rel = 1e-6 -solver.attributes.reduced_tol_ktratio = 1e-6 diff --git a/infeasible/ACOPF/dual.h5.gz b/infeasible/ACOPF/dual.h5.gz deleted file mode 100644 index 6e85d7da084a86b339eaa8827b6e718997bfc1a6..0000000000000000000000000000000000000000 --- a/infeasible/ACOPF/dual.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2c20d000d865807da3417cae7ad14c717711c29282fb9b5241c6cad096dd7305 -size 16104833 diff --git a/infeasible/ACOPF/meta.h5.gz b/infeasible/ACOPF/meta.h5.gz deleted file mode 100644 index 31804c14ee257d80278cc3e5bee5e064eb0cfefe..0000000000000000000000000000000000000000 --- a/infeasible/ACOPF/meta.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:7c87ec1c8b5326fce02d19ed9ce6a9c9489211da93619f97a736421eea41e229 -size 305417 diff --git a/infeasible/ACOPF/primal.h5.gz b/infeasible/ACOPF/primal.h5.gz deleted file 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