  Online weighted matching problem is a fundamental problem in machine learning
due to its numerous applications. Despite many efforts in this area, existing
algorithms are either too slow or don't take $\mathrm{deadline}$ (the longest
time a node can be matched) into account. In this paper, we introduce a market
model with $\mathrm{deadline}$ first. Next, we present our two optimized
algorithms (\textsc{FastGreedy} and \textsc{FastPostponedGreedy}) and offer
theoretical proof of the time complexity and correctness of our algorithms. In
\textsc{FastGreedy} algorithm, we have already known if a node is a buyer or a
seller. But in \textsc{FastPostponedGreedy} algorithm, the status of each node
is unknown at first. Then, we generalize a sketching matrix to run the original
and our algorithms on both real data sets and synthetic data sets. Let
$\epsilon \in (0,0.1)$ denote the relative error of the real weight of each
edge. The competitive ratio of original \textsc{Greedy} and
\textsc{PostponedGreedy} is $\frac{1}{2}$ and $\frac{1}{4}$ respectively. Based
on these two original algorithms, we proposed \textsc{FastGreedy} and
\textsc{FastPostponedGreedy} algorithms and the competitive ratio of them is
$\frac{1 - \epsilon}{2}$ and $\frac{1 - \epsilon}{4}$ respectively. At the same
time, our algorithms run faster than the original two algorithms. Given $n$
nodes in $\mathbb{R} ^ d$, we decrease the time complexity from $O(nd)$ to
$\widetilde{O}(\epsilon^{-2} \cdot (n + d))$.

Data Structures and Algorithms;Machine Learning