  Non-negative least squares (NNLS) problem is one of the most important
fundamental problems in numeric analysis. It has been widely used in scientific
computation and data modeling. In big data, the limitations of algorithm speed
and accuracy are typical challenges. In this paper, we propose fast and robust
anti-lopsided algorithm with high accuracy that is totally based on the first
order methods. The main idea of our algorithm is to transform the original NNLS
into an equivalent non-negative quadratic programming, which significantly
reduce the scaling problem of variables. The proposed algorithm can reach high
accuracy and fast speed with linear convergence $(1 - \frac{1}{2||Q||_2})^k$
where $\sqrt{n} \leq ||Q||_2 \leq n$, and $n$ is the dimension size of
solutions. The experiments on large matrices clearly show the high performance
of the proposed algorithm in comparison to the state-of-the-art algorithms.
