Problem:
Max Score: $600$ Points

Problem Statement

Sigma and his brother Sugim are in the $H \times W$ grid. They wants to buy some souvenirs.

Their start position is upper-left cell, and the goal position is lower-right cell.

Some cells has a souvenir shop. At $i$-th row and $j$-th column, there is $a_{i, j}$ souvenirs.

In one move, they can go left, right, down, and up cell.

But they have little time, so they can move only $H+W-2$ times.

They wanted to buy souvenirs as many as possible, but they had no computer, so they couldn't get the maximal numbers of souvenirs.

Write a program and calculate the maximum souvenirs they can get, and help them.

Input

The input is given from standard input in the following format.

$H \ W$
$a_{1, 1} \ a_{1, 2} \ \cdots \ a_{1, W}$
$a_{2, 1} \ a_{2, 2} \ \cdots \ a_{2, W}$
$\vdots \ \ \ \ \ \ \ \ \ \ \vdots \ \ \ \ \ \ \ \ \ \ \vdots$
$a_{H, 1} \ a_{H, 2} \ \cdots \ a_{H, W}$

Output

Print the maximum number of souvenirs they can get.

Constraints

$1 \le H, W \le 200$

$0 \le a_{i, j} \le 10^5$

Subtasks

Subtask 1 [ 50 points ]

The testcase in the subtask satisfies $1 \le H \le 2$.

Subtask 2 [ 80 points ]

The testcase in the subtask satisfies $1 \le H \le 3$.

Subtask 3 [ 120 points ]

The testcase in the subtask satisfies $1 \le H, W \le 7$.

Subtask 4 [ 150 points ]

The testcase in the subtask satisfies $1 \le H, W \le 30$.

Subtask 5 [ 200 points ]

There are no additional constraints.

Sample Input 1

3 3
1 0 5
2 2 3
4 2 4

Sample Output 1

21

The cell at $i$-th row and $j$-th column is denoted $(i, j)$.

In this case, one of the optimal solution is this:

Sigma moves $(1, 1) -> (1, 2) -> (1, 3) -> (2, 3) -> (3, 3)$.

Sugim moves $(1, 1) -> (2, 1) -> (3, 1) -> (3, 2) -> (3, 3)$.

Then, they can get $21$ souvernirs.

Sample Input 2

6 6
1 2 3 4 5 6
8 6 9 1 2 0
3 1 4 1 5 9
2 6 5 3 5 8
1 4 1 4 2 1
2 7 1 8 2 8

Sample Output 2

97

Writer : square1001

Solution in Lua (LuaJIT 2.1.0) (judge verdict: Time Limit Exceeded)

local mmi, mma = math.min, math.max
local MinCostFlow = {}

MinCostFlow.initialize = function(self, n, spos, tpos, inf)
  self.n = n
  self.spos, self.tpos = spos, tpos
  self.inf = inf
  -- edge_dst[src][i] := dst
  self.edge_dst = {}
  -- edge_cost[src][i] := cost from src to edge_dst[src][i]
  self.edge_cost = {}
  -- edge_cap[src][i] := capacity from src to edge_dst[src][i]
  self.edge_cap = {}
  -- initial capacity. corresponding to edge_cap
  self.edge_initialcap = {}
  -- edge_dst_invedge_idx[src][i] := "j" where edge_dst[dst][j] == src
  -- in this case, edge_dst_invedge_idx[dst][j] should be "i".
  self.edge_dst_invedge_idx = {}
  -- len[v] := length from spos. len[spos] := 0
  self.len = {}
  -- sub_graph_flag[v] := whether to contains the vertex v in the sub-graph or not
  self.sub_graph_flag = {}
  -- sub_graph_v[i] := list of vertexes that are contained in the sub-graph. from tpos to spos.
  self.sub_graph_v = {}
  -- sub_graph_edgeidx[i] := edge index from sub_graph_v[i + 1] to sub_graph_v[i]
  self.sub_graph_edgeidx = {}
  -- sub_graph_size := the size of sub_graph_v.
  -- may not equal to #sub_graph_v (because not cleared).
  self.sub_graph_size = 0
  for i = 1, n do
    self.edge_dst[i] = {}
    self.edge_cost[i] = {}
    self.edge_cap[i] = {}
    self.edge_initialcap[i] = {}
    self.edge_dst_invedge_idx[i] = {}
    self.len[i] = 0
    self.sub_graph_flag[i] = false
  end
end

MinCostFlow.addEdge = function(self, src, dst, cost, cap)
  table.insert(self.edge_dst[src], dst)
  table.insert(self.edge_cost[src], cost)
  table.insert(self.edge_cap[src], cap)
  table.insert(self.edge_initialcap[src], cap)
  table.insert(self.edge_dst_invedge_idx[src], 1 + #self.edge_dst[dst])
  table.insert(self.edge_dst[dst], src)
  table.insert(self.edge_cost[dst], -cost)
  table.insert(self.edge_cap[dst], 0)--invcap
  table.insert(self.edge_initialcap[dst], 0)--invcap
  table.insert(self.edge_dst_invedge_idx[dst], #self.edge_dst[src])
end
MinCostFlow.invwalk_recursive = function(self, invsrc)
  if invsrc == self.spos then return true end
  local edge_dst, edge_cap, edge_cost = self.edge_dst, self.edge_cap, self.edge_cost
  local edge_dst_invedge_idx = self.edge_dst_invedge_idx
  local len = self.len
  local sub_graph_flag = self.sub_graph_flag
  local sub_graph_v = self.sub_graph_v
  local sub_graph_edgeidx = self.sub_graph_edgeidx
  for i = 1, #edge_dst[invsrc] do
    local invdst = edge_dst[invsrc][i]
    local j = edge_dst_invedge_idx[invsrc][i]
    if 0 < edge_cap[invdst][j]
    and len[invdst] + edge_cost[invdst][j] == len[invsrc]
    and not sub_graph_flag[invdst] then
      self.sub_graph_size = self.sub_graph_size + 1
      sub_graph_v[self.sub_graph_size] = invdst
      sub_graph_edgeidx[self.sub_graph_size - 1] = j
      sub_graph_flag[invdst] = true
      if self:invwalk_recursive(invdst) then
        return true
      else
        self.sub_graph_flag[invdst] = false
        self.sub_graph_size = self.sub_graph_size - 1
      end
    end
  end
  return false
end

MinCostFlow.makeSubGraph = function(self)
  local inf = self.inf
  local len = self.len
  local n = self.n
  local edge_dst, edge_cap = self.edge_dst, self.edge_cap
  local edge_dst_invedge_idx = self.edge_dst_invedge_idx
  local edge_cost = self.edge_cost
  local sub_graph_v = self.sub_graph_v
  local sub_graph_edgeidx = self.sub_graph_edgeidx
  local sub_graph_flag = self.sub_graph_flag
  for i = 1, self.n do
    len[i] = inf
    sub_graph_flag[i] = false
  end
  -- Bellman-Ford
  local updated1, updated2 = {}, {}
  for i = 1, n do
    updated1[i] = true
  end
  len[self.spos] = 0
  for irp = 1, n do
    local updsrc = irp % 2 == 1 and updated1 or updated2
    local upddst = irp % 2 == 1 and updated2 or updated1
    for i = 1, n do
      upddst[i] = false
    end
    for src = 1, n do
      if updsrc[src] then
        local eddst, edcap, edcost = edge_dst[src], edge_cap[src], edge_cost[src]
        for i = 1, #eddst do
          if 0 < edcap[i] then
            local dst = eddst[i]
            local cost = edcost[i]
            if len[src] + cost < len[dst] then
              len[dst] = len[src] + cost
              upddst[dst] = true
            end
          end
        end
      end
    end
  end
  self.sub_graph_size = 0
  if inf <= len[self.tpos] then
    return 0
  end
  -- restore route (from tpos to spos)
  self.sub_graph_size = 1
  sub_graph_v[1] = self.tpos
  self:invwalk_recursive(self.tpos)
  local min_capacity = inf
  for i = self.sub_graph_size, 2, -1 do
    local src = sub_graph_v[i]
    local j = sub_graph_edgeidx[i - 1]
    min_capacity = mmi(min_capacity, edge_cap[src][j])
  end
  return min_capacity
end

MinCostFlow.flow = function(self, capacity)
  local edge_dst, edge_cap = self.edge_dst, self.edge_cap
  local edge_dst_invedge_idx = self.edge_dst_invedge_idx
  local sub_graph_v = self.sub_graph_v
  local sub_graph_edgeidx = self.sub_graph_edgeidx
  for i = self.sub_graph_size, 2, -1 do
    local src = sub_graph_v[i]
    local dst = sub_graph_v[i - 1]
    local j = sub_graph_edgeidx[i - 1]
    edge_cap[src][j] = edge_cap[src][j] - capacity
    local k = edge_dst_invedge_idx[src][j]
    edge_cap[dst][k] = edge_cap[dst][k] + capacity
  end
end

MinCostFlow.getMinCostFlow = function(self, amount, invalid)
  local ret = 0
  local cap = self:makeSubGraph()
  while 0 < cap do
    cap = mmi(amount, cap)
    ret = ret + self.len[self.tpos] * cap
    self:flow(cap)
    amount = amount - cap
    if 0 < amount then
      cap = self:makeSubGraph()
    else
      break
    end
  end
  if 0 < amount then return invalid end
  return ret
end

local h, w = io.read("*n", "*n")
local s = 2 * h * w + 1
local t = 2 * h * w + 2
MinCostFlow:initialize(t, s, t, 1000000007 * 100)
MinCostFlow:addEdge(s, 1, 0, 2)
MinCostFlow:addEdge(2 * h * w, t, 0, 2)
for i = 1, h do
  for j = 1, w - 1 do
    local idx = (i - 1) * w + j
    MinCostFlow:addEdge(idx + h * w, idx + 1, 0, 2)
  end
end
for i = 1, h - 1 do
  for j = 1, w do
    local idx = (i - 1) * w + j
    MinCostFlow:addEdge(idx + h * w, idx + w, 0, 2)
  end
end
for i = 1, h do
  for j = 1, w do
    local idx = (i - 1) * w + j
    local a = io.read("*n")
    MinCostFlow:addEdge(idx, idx + h * w, 0, 1)
    MinCostFlow:addEdge(idx, idx + h * w, -a, 1)
  end
end
print(math.abs(MinCostFlow:getMinCostFlow(2)))