我可以通过pyspark在Spark中使用L-BFGS优化器吗？

from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.linalg import Vectors

from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()

# 创建一个示例数据集
data = [(1.0, Vectors.dense([0.0, 1.1, 0.1])),
        (0.0, Vectors.dense([2.0, 1.0, -1.0])),
        (0.0, Vectors.dense([2.0, 1.3, 1.0])),
        (1.0, Vectors.dense([0.0, 1.2, -0.5]))]

df = spark.createDataFrame(data, ["label", "features"])

# 将特征列转换为向量
assembler = VectorAssembler(inputCols=["features"], outputCol="features_vector")
df = assembler.transform(df)

# 创建Logistic回归模型
lr = LogisticRegression(maxIter=10, regParam=0.01, elasticNetParam=0.8, family="binomial")

# 使用L-BFGS优化器
lr.setSolver("l-bfgs")

# 拟合模型
model = lr.fit(df)

# 进行预测
predictions = model.transform(df)