The shortest path problem is a classic problem in mathematics and computer science with applications in
Variations of the methods we discuss in this lecture are used millions of times every day, in applications such as
For us, the shortest path problem also provides a nice introduction to the logic of dynamic programming.
Dynamic programming is an extremely powerful optimization technique that we apply in many lectures on this site.
The shortest path problem is one of finding how to traverse a graph from one specified node to another at minimum cost.
Consider the following graph
We wish to travel from node (vertex) A to node G at minimum cost.
Possible interpretations of the graph include
For this simple graph, a quick scan of the edges shows that the optimal paths are
For large graphs we need a systematic solution.
Let $ J(v) $ denote the minimum cost-to-go from node $ v $, understood as the total cost from $ v $ if we take the best route.
Suppose that we know $ J(v) $ for each node $ v $, as shown below for the graph from the preceding example
Note that $ J(G) = 0 $.
The best path can now be found as follows
$$ \min_{w \in F_v} \{ c(v, w) + J(w) \} \tag{1} $$
where
Hence, if we know the function $ J $, then finding the best path is almost trivial.
But how to find $ J $?
Some thought will convince you that, for every node $ v $, the function $ J $ satisfies
$$ J(v) = \min_{w \in F_v} \{ c(v, w) + J(w) \} \tag{2} $$
This is known as the Bellman equation, after the mathematician Richard Bellman.
The standard algorithm for finding $ J $ is to start with
$$ J_0(v) = M \text{ if } v \not= \text{ destination, else } J_0(v) = 0 \tag{3} $$
where $ M $ is some large number.
Now we use the following algorithm
In general, this sequence converges to $ J $—the proof is omitted.
Use the algorithm given above to find the optimal path (and its cost) for the following graph.
using InstantiateFromURL
# optionally add arguments to force installation: instantiate = true, precompile = true
github_project("QuantEcon/quantecon-notebooks-julia", version = "0.8.0")
using LinearAlgebra, Statistics
graph = Dict(zip(0:99, [[(14, 72.21), (8, 11.11), (1, 0.04)],[(13, 64.94), (6, 20.59), (46, 1247.25)],[(45, 1561.45), (31, 166.8), (66, 54.18)],[(11, 42.43), (6, 2.06), (20, 133.65)],[(7, 1.02), (5, 0.73), (75, 3706.67)],[(11, 34.54),(7, 3.33),(45, 1382.97)],[(10, 13.1), (9, 0.72), (31, 63.17)],[(10, 5.85), (9, 3.15), (50, 478.14)], [(12, 3.18), (11, 7.45), (69, 577.91)],[(20, 16.53), (13, 4.42), (70, 2454.28)],[(16, 25.16), (12, 1.87), (89, 5352.79)],[(20, 65.08), (18, 37.55), (94, 4961.32)],[(28, 170.04), (24, 34.32), (84, 3914.62)],[(40, 475.33), (38, 236.33), (60, 2135.95)],[(24, 38.65), (16, 2.7),(67, 1878.96)],[(18, 2.57),(17, 1.01),(91, 3597.11)],[(38, 278.71),(19, 3.49),(36, 392.92)],[(23, 26.45), (22, 24.78), (76, 783.29)],[(28, 55.84), (23, 16.23), (91, 3363.17)],[(28, 70.54), (20, 0.24), (26, 20.09)],[(33, 145.8), (24, 9.81),(98, 3523.33)],[(31, 27.06),(28, 36.65),(56, 626.04)], [(40, 124.22), (39, 136.32), (72, 1447.22)],[(33, 22.37), (26, 2.66), (52, 336.73)],[(28, 14.25), (26, 1.8), (66, 875.19)],[(35, 45.55),(32, 36.58),(70, 1343.63)],[(42, 122.0),(27, 0.01), (47, 135.78)],[(43, 246.24), (35, 48.1),(65, 480.55)],[(36, 15.52), (34, 21.79), (82, 2538.18)],[(33, 12.61), (32, 4.22),(64, 635.52)], [(35, 13.95), (33, 5.61), (98, 2616.03)],[(44, 125.88),(36, 20.44), (98, 3350.98)],[(35, 1.46), (34, 3.33), (97, 2613.92)], [(47, 111.54), (41, 3.23), (81, 1854.73)],[(48, 129.45), (42, 51.52), (73, 1075.38)],[(50, 78.81), (41, 2.09), (52, 17.57)], [(57, 260.46), (54, 101.08), (71, 1171.6)],[(46, 80.49),(38, 0.36), (75, 269.97)],[(42, 8.78), (40, 1.79), (93, 2767.85)],[(41, 1.34), (40, 0.95), (50, 39.88)],[(54, 53.46), (47, 28.57), (75, 548.68)], [(54, 162.24), (46, 0.28), (53, 18.23)],[(72, 437.49), (47, 10.08), (59, 141.86)],[(60, 116.23), (54, 95.06), (98, 2984.83)], [(47, 2.14), (46, 1.56), (91, 807.39)],[(49, 15.51), (47, 3.68), (58, 79.93)],[(67, 65.48), (57, 27.5), (52, 22.68)],[(61, 172.64), (56, 49.31), (50, 2.82)],[(60, 66.44), (59, 34.52), (99, 2564.12)], [(56, 10.89), (50, 0.51), (78, 53.79)],[(55, 20.1), (53, 1.38), (85, 251.76)],[(60, 73.79),(59, 23.67),(98, 2110.67)], [(66, 123.03), (64, 102.41), (94, 1471.8)],[(67, 88.35),(56, 4.33), (72, 22.85)],[(73, 238.61), (59, 24.3), (88, 967.59)],[(64, 60.8), (57, 2.13), (84, 86.09)],[(61, 11.06), (57, 0.02), (76, 197.03)], [(60, 7.01), (58, 0.46), (86, 701.09)],[(65, 34.32), (64, 29.85), (83, 556.7)],[(71, 0.67), (60, 0.72), (90, 820.66)],[(67, 1.63), (65, 4.76), (76, 48.03)],[(64, 4.88), (63, 0.95), (98, 1057.59)], [(76, 38.43), (64, 2.94), (91, 132.23)],[(75, 56.34), (72, 70.08), (66, 4.43)],[(76, 11.98), (65, 0.3), (80, 47.73)],[(73, 33.23), (66, 0.64), (94, 594.93)],[(73, 37.53), (68, 2.66), (98, 395.63)], [(70, 0.98), (68, 0.09), (82, 153.53)],[(71, 1.66), (70, 3.35), (94, 232.1)],[(73, 8.99), (70, 0.06), (99, 247.8)],[(73, 8.37), (72, 1.5), (76, 27.18)],[(91, 284.64), (74, 8.86), (89, 104.5)], [(92, 133.06), (84, 102.77), (76, 15.32)],[(90, 243.0), (76, 1.4), (83, 52.22)],[(78, 8.08), (76, 0.52), (81, 1.07)],[(77, 1.19), (76, 0.81), (92, 68.53)],[(78, 2.36), (77, 0.45), (85, 13.18)], [(86, 64.32), (78, 0.98), (80, 8.94)],[(81, 2.59), (98, 355.9)],[(91, 22.35), (85, 1.45), (81, 0.09)],[(98, 264.34), (88, 28.78), (92, 121.87)],[(92, 99.89), (89, 39.52), (94, 99.78)],[(93, 11.99), (88, 28.05), (91, 47.44)],[(88, 5.78), (86, 8.75), (94, 114.95)], [(98, 121.05), (94, 30.41), (89, 19.14)],[(89, 4.9), (87, 2.66), (97, 94.51)],[(97, 85.09)],[(92, 21.23), (91, 11.14), (88, 0.21)], [(98, 6.12), (91, 6.83), (93, 1.31)],[(99, 82.12), (97, 36.97)], [(99, 50.99), (94, 10.47), (96, 23.53)],[(97, 22.17)],[(99, 34.68), (97, 11.24), (96, 10.83)],[(99, 32.77), (97, 6.71), (94, 0.19)], [(96, 2.03), (98, 5.91)],[(99, 0.27), (98, 6.17)],[(99, 5.87), (97, 0.43), (98, 3.32)],[(98, 0.3)],[(99, 0.33)],[(99, 0.0)]]))
Dict{Int64,Array{Tuple{Int64,Float64},1}} with 100 entries: 68 => [(71, 1.66), (70, 3.35), (94, 232.1)] 2 => [(45, 1561.45), (31, 166.8), (66, 54.18)] 89 => [(99, 82.12), (97, 36.97)] 11 => [(20, 65.08), (18, 37.55), (94, 4961.32)] 39 => [(41, 1.34), (40, 0.95), (50, 39.88)] 46 => [(67, 65.48), (57, 27.5), (52, 22.68)] 85 => [(89, 4.9), (87, 2.66), (97, 94.51)] 25 => [(35, 45.55), (32, 36.58), (70, 1343.63)] 55 => [(64, 60.8), (57, 2.13), (84, 86.09)] 42 => [(72, 437.49), (47, 10.08), (59, 141.86)] 29 => [(33, 12.61), (32, 4.22), (64, 635.52)] 58 => [(65, 34.32), (64, 29.85), (83, 556.7)] 66 => [(73, 37.53), (68, 2.66), (98, 395.63)] 59 => [(71, 0.67), (60, 0.72), (90, 820.66)] 8 => [(12, 3.18), (11, 7.45), (69, 577.91)] 74 => [(78, 8.08), (76, 0.52), (81, 1.07)] 95 => [(99, 0.27), (98, 6.17)] 57 => [(60, 7.01), (58, 0.46), (86, 701.09)] 20 => [(33, 145.8), (24, 9.81), (98, 3523.33)] 90 => [(99, 50.99), (94, 10.47), (96, 23.53)] 14 => [(24, 38.65), (16, 2.7), (67, 1878.96)] 31 => [(44, 125.88), (36, 20.44), (98, 3350.98)] 78 => [(81, 2.59), (98, 355.9)] 70 => [(73, 8.37), (72, 1.5), (76, 27.18)] 33 => [(47, 111.54), (41, 3.23), (81, 1854.73)] ⋮ => ⋮
The cost from node 68 to node 71 is 1.66 and so on.
function update_J!(J, graph)
next_J = Dict()
for node in keys(graph)
if node == 99
next_J[node] = 0
else
next_J[node] = minimum(cost + J[dest] for (dest, cost) in graph[node])
end
end
return next_J
end
function print_best_path(J, graph)
sum_costs = 0.0
current_location, destination = extrema(keys(graph))
while current_location != destination
println("node $current_location")
running_min = 1e10
minimizer_dest = Inf
minimizer_cost = 1e10
for (dest, cost) in graph[current_location]
cost_of_path = cost + J[dest]
if cost_of_path < running_min
running_min = cost_of_path
minimizer_cost = cost
minimizer_dest = dest
end
end
current_location = minimizer_dest
sum_costs += minimizer_cost
end
sum_costs = round(sum_costs, digits = 2)
println("node $destination\nCost: $sum_costs")
end
J = Dict((node => Inf) for node in keys(graph))
while true
next_J = update_J!(J, graph)
if next_J == J
break
else
J = next_J
end
end
print_best_path(J, graph)
node 0 node 8 node 11 node 18 node 23 node 33 node 41 node 53 node 56 node 57 node 60 node 67 node 70 node 73 node 76 node 85 node 87 node 88 node 93 node 94 node 96 node 97 node 98 node 99 Cost: 160.55