Problem:
Score : 500 points

Problem Statement

We have a grid with H horizontal rows and W vertical columns. Let (i,j) denote the square at the i-th row from the top and the j-th column from the left.

The square (i, j) has two numbers A_{ij} and B_{ij} written on it.

First, for each square, Takahashi paints one of the written numbers red and the other blue.

Then, he travels from the square (1, 1) to the square (H, W). In one move, he can move from a square (i, j) to the square (i+1, j) or the square (i, j+1). He must not leave the grid.

Let the unbalancedness be the absolute difference of the sum of red numbers and the sum of blue numbers written on the squares along Takahashi's path, including the squares (1, 1) and (H, W).

Takahashi wants to make the unbalancedness as small as possible by appropriately painting the grid and traveling on it.

Find the minimum unbalancedness possible.

Constraints

2 \leq H \leq 80

2 \leq W \leq 80

0 \leq A_{ij} \leq 80

0 \leq B_{ij} \leq 80

All values in input are integers.

Input

Input is given from Standard Input in the following format:

H W
A_{11} A_{12} \ldots A_{1W}
:
A_{H1} A_{H2} \ldots A_{HW}
B_{11} B_{12} \ldots B_{1W}
:
B_{H1} B_{H2} \ldots B_{HW}

Output

Print the minimum unbalancedness possible.

Sample Input 1

2 2
1 2
3 4
3 4
2 1

Sample Output 1

0

By painting the grid and traveling on it as shown in the figure below, the sum of red numbers and the sum of blue numbers are 3+3+1=7 and 1+2+4=7, respectively, for the unbalancedness of 0.

Sample Input 2

2 3
1 10 80
80 10 1
1 2 3
4 5 6

Sample Output 2

2

Solution in Rust (1.15.1) (judge verdict: Accepted)

fn read_ints() -> Vec<i32> {
    let mut s = String::new();
    std::io::stdin().read_line(&mut s).unwrap();
    let s = s.trim_right().to_owned();
    let ws = s.split_whitespace();
    let mut ints = Vec::new();
    for num_s in ws {
        ints.push(num_s.parse().unwrap());
    }
    ints
}

struct ThreeTensor<T> {
    vec: Vec<T>,
    size1: usize,
    size2: usize,
    size3: usize,
}

impl<T: Clone + Copy> ThreeTensor<T> {
    fn new(size1: usize, size2: usize, size3: usize, init: T) -> Self {
        let mut vec = Vec::new();
        vec.resize(size1 * size2 * size3, init);
        ThreeTensor {
            //vec, size1, size2, size3
            vec: vec,
            size1: size1,
            size2: size2,
            size3: size3,
        }
    }

    fn at(&self, i: usize, j: usize, k: usize) -> T {
        self.vec[i * self.size2 * self.size3 + j * self.size3 + k]
    }

    fn set(&mut self, i: usize, j: usize, k: usize, v: T) {
        self.vec[i * self.size2 * self.size3 + j * self.size3 + k] = v;
    }
}

fn main() {
    let ints = read_ints();
    let h = ints[0] as usize;
    let w = ints[1] as usize;
    let mut a = Vec::new();
    let mut b = Vec::new();
    for _ in 0..h {
        a.push(read_ints());
    }
    for _ in 0..h {
        b.push(read_ints());
    }
    let a = a;
    let b = b;
    let mut abs_amb = Vec::new();
    for i in 0..h {
        let mut abs_amb_ = Vec::new();
        for j in 0..w {
            abs_amb_.push((a[i][j] - b[i][j]).abs());
        }
        abs_amb.push(abs_amb_);
    }
    let abs_amb = abs_amb;

    let m = (h + w) * 80;
    let mut bias_cand_tensor = ThreeTensor::new(h, w, m, false);
    for i in 0..h {
        for j in 0..w {
            let abs_amb_ = abs_amb[i][j];
            if i == 0 && j == 0 {
                bias_cand_tensor.set(i, j, abs_amb_ as usize, true);
            } else {
                if i > 0 {
                    for k in 0..m {
                        let new_k = (k as i32 + abs_amb_).abs() as usize;
                        if new_k < m {
                            let new_flg = bias_cand_tensor.at(i, j, new_k)
                                || bias_cand_tensor.at(i - 1, j, k);
                            bias_cand_tensor.set(i, j, new_k, new_flg);
                        }

                        let new_k = (k as i32 - abs_amb_).abs() as usize;
                        if new_k < m {
                            let new_flg = bias_cand_tensor.at(i, j, new_k)
                                || bias_cand_tensor.at(i - 1, j, k);
                            bias_cand_tensor.set(i, j, new_k, new_flg);
                        }
                    }
                }

                if j > 0 {
                    for k in 0..m {
                        let new_k = (k as i32 + abs_amb_).abs() as usize;
                        if new_k < m {
                            let new_flg = bias_cand_tensor.at(i, j, new_k)
                                || bias_cand_tensor.at(i, j - 1, k);
                            bias_cand_tensor.set(i, j, new_k, new_flg);
                        }

                        let new_k = (k as i32 - abs_amb_).abs() as usize;
                        if new_k < m {
                            let new_flg = bias_cand_tensor.at(i, j, new_k)
                                || bias_cand_tensor.at(i, j - 1, k);
                            bias_cand_tensor.set(i, j, new_k, new_flg);
                        }
                    }
                }
            }
        }
    }

    let mut min_bias = 999999;
    for k in 0..m {
        if bias_cand_tensor.at(h - 1, w - 1, k) {
            min_bias = k;
            break;
        }
    }
    println!("{}", min_bias);
}