## Define single source shortest path problem

### Define single source shortest path algorithm

shown within each vertex is its shortest-path weight from source s.. for all v v', the unique simple path from s to v in g' is a shortest path from s to v in g. a shortest path p from source s to vertex v has no more weight than any other path from s to v. greedy strategies do not always yield optimal results in general, but as the following theorem and its corollary show, dijkstra's algorithm does indeed compute shortest paths.. prove that with this scheme, if g contains no negative-weight cycles that are reachable from the source vertex s, then after only |v| /2 passes over the edges, d[v] = (s, v) for all vertices v v..It remains, therefore, to prove the last property of shortest-paths trees: for all v v, the unique simple path in g is a shortest path from s to v in g. show that an infinite sequence of relaxations of the edges of g can always be constructed such that every relaxation causes a shortest-path estimate to change., first using vertex s as the source and then using vertex y as the source.= float("inf") else "inf" ,Print ("following are shortest distances from source %d " % s). complete the proof of the lemma, we must now show that for any vertex v v, there is at most one path from s to v in the graph g. use the following api for computing the shortest paths of an edge-weighted digraph:We prepare some test data:Contains 8 vertices and 15 edges. moreover, no algorithms for this problem are known that run asymptotically faster than the best single-source algorithms in the worst case. similarly, there is only one path from s to b, and so (s, b) = w(s, a) + w(a, b) = 3 + (-4) = - 1.''' the function to find shortest paths from given vertex. a feasible solution or determine that no feasible solution exists for the following system of difference constraints:Find a feasible solution or determine that no feasible solution exists for the following system of difference constraints:Can any shortest-path weight from the new vertex v0 in a constraint graph be positive?-pair shortest-path problem: find a shortest path from u to v for given vertices u and v. often wish to compute not only shortest-path weights, but the vertices on the shortest paths as well. and only if there exists at least one directed path from s to v.(hint: show that a shortest path to any vertex on the minimum mean-weight cycle can be extended along the cycle to make a shortest path to the next vertex on the cycle. then, after the graph is initialized by initialize-single-source(g, s), we have d[v] = (s, v), and this equality is maintained as an invariant over any sequence of relaxation steps on the edges of g. in a shortest-paths problem, we are given a weighted, directed graph g = (v, e), with weight function w : e r mapping edges to real-valued weights. let s v be the source vertex, and let us define [v] as usual: [v] is the predecessor of v on some shortest path to v from source s if v v - {s} is reachable from s, and nil otherwise. no path from s to a vertex on the cycle can be a shortest path--a lesser-weight path can always be found that follows the proposed "shortest" path and then traverses the negative-weight cycle. a path through this dag represents a sequence of jobs that must be performed in a particular order. let s v be the source vertex, and let the graph be initialized by initialize-single-source(g, s). suppose that the weight of the path from u to v along the cycle is x.// c++ program to find single source shortest paths for directed acyclic graphs. shortest path from vertex s to vertex t is a directed path. we can calculate single source shortest distances in o(v+e) time for dags. an edge-weighted dag:Create an edge-weighted dag with a source s, a sink t, and two. process of relaxing1 an edge (u, v) consists of testing whether we can improve the shortest path to v found so far by going through u and, if so, updating d[v] and [v]. suppose that graph g contains no negative-weight cycles that are reachable from the source s. to handle cases in which shortest-path weights are or -. g = (v, e) be a weighted, directed graph with weight function w : e r, let s v be a source vertex, and let be a shortest path in g for some vertices u, v v. now, change the weight of edge (y, v) to 4 and run the algorithm again, using z as the source. first must show that a path from s exists for each vertex in v. in fact, dijkstra's algorithm is a greedy algorithm, and the floyd-warshall algorithm, which finds shortest paths between all pairs of vertices (see chapter 26), is a dynamic-programming algorithm. because g is reachable from f, we can also find paths with arbitrarily large negative weights from s to g, and (s, g) = -.

### Define single source shortest path problem

scaling algorithm solves a problem by initially considering only the highest-order bit of each relevant input value (such as an edge weight). a relaxation step may decrease the value of the shortest-path estimate d[v] and update v's predecessor field [v]. by traversing the negative-weight cycle e, f, e arbitrarily many times, we can find paths from s to e with arbitrarily large negative weights, and so (s, e) = -. in dijkstra's algorithm and the shortest-paths algorithm for directed acyclic graphs, each edge is relaxed exactly once. we conclude that the bellman-ford algorithm returns true if graph g contains no negative-weight cycles reachable from the source, and false otherwise.-paths algorithms typically exploit the property that a shortest path between two vertices contains other shortest paths within it. let (s, v) be the weight of a shortest path from s to v, and let k(s, v) be the weight of a shortest path from s to v consisting of exactly k edges. consider the parallel precedence-constrained job scheduling problem:Given a set of jobs of specified duration to be completed, with precedence. if g contains no negative-weight cycles that are reachable from s, then the algorithm returns true, we have d[v] = (s,v) for all vertices v v, and the predecessor subgraph g is a shortest-paths tree rooted at s. dijkstra's algorithm, which solves the single-source shortest-paths problem when all edges have nonnegative weight, is given in section 25. the path p is simple, and so k |v| - 1. represent the shortest paths with two vertex-indexed arrays:Edges on the shortest-paths tree:Edgeto[v] is the the last edge on a shortest path from s to v. a directed graph g(v,e) with weighted edges w(u,v), define the path weight of a path p as. single-source shortest-paths algorithms in this chapter are all based on a technique known as relaxation. for a graph with no negative weights, we can do better and calculate single source shortest distances in o(e + vlogv) time using dijkstra’s algorithm. then, after the graph is initialized by initialize-single-source(g, s), the predecessor subgraph g forms a rooted tree with root s, and any sequence of relaxation steps on edges of g maintains this property as an invariant. bellman describes the relation of shortest paths to difference constraints. the single-source shortest-paths problem that results can then be solved using the bellman-ford algorithm, thereby also solving the linear-programming problem. similarly, the shortest path from s to d is s, c, d, with weight (s, d) = w(s, c) + w(c, d) = 11. for several reasons, though, it is important to understand the setup of linear-programming problems. pair shortest paths - one technique is to use single source shortest path for each vertex, but later we will see a more efficient algorithm. g be an arbitrary weighted, directed graph with a negative-weight cycle reachable from the source vertex s. relaxing the edges of a weighted dag (directed acyclic graph) g = (v, e) according to a topological sort of its vertices, we can compute shortest paths from a single source in (v + e) time..That solves the single-source shortest paths problems in undirected graphs. now consider an algorithm for finding shortest paths that is simpler and faster. can solve the single-source longest paths problems in edge-weighted dags. dag-shortest-paths, replacing "" by "-" in line 2 of initialize-single-source and ">" by "< " in the relax procedure. is a shortest-paths algorithm that works on unweighted graphs, that is, graphs in which each edge can be considered to have unit weight. if we decompose path p into then w(p) = w(p1i) + w(pij) + w(pjk). give an efficient algorithm to find the most reliable path between two given vertices. if there is no such cycle, the algorithm produces the shortest paths and their weights. let s v be the source vertex, and let g be initialized by initialize-single-source(g, s).-3) to prove that for each vertex v v, there is a unique path from s to v in g. edge weights are relatively small integers, more efficient algorithms can be used to solve the single-source shortest-paths problem. if there is a negative-weight cycle reachable from s, however, shortest-path weights are not well defined. it shows step by step process of finding shortest paths. single source shortest path algorithms use the same notation as bfs (see lecture 17) with predecessor π and distance d fields for each vertex. the shortest-path estimate of each vertex is shown within the vertex.

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by reversing the direction of each edge in the graph, we can reduce this problem to a single-source problem.. now, define as the shortest-path weight from s to v using the weight function . the algorithm returns true if and only if the graph contains no negative-weight cycles that are reachable from the source. if there is a negative-weight cycle on some path from s to v, we define (s,v) = -. that every subpath on a shortest path from v to w is also a shortest path. that is, suppose that there are two simple paths from s to some vertex v: p1, which can be decomposed into and p2, which can be decomposed into , where x y. problems can be expressed as linear programs, and for this reason much work has gone into algorithms for linear programming. gives a linear-time algorithm for computing shortest paths from a single source in directed acyclic graphs. g = (v, e) be a weighted, directed graph g = (v, e) with weight function w: e r and source vertex s. bellman-ford algorithm uses relaxation to find single source shortest paths on directed graphs that may contain negative weight edges. in fact, there is more than one solution to this problem. shortestpath(self, s):# mark all the vertices as not visited. the following lemma and its corollary state the optimal-substructure property of shortest paths more precisely. by definition, a shortest-path weight (s, v) is finite if and only if v is reachable from s, and thus the vertices that are reachable from s are exactly those with finite d values. , vk be a shortest path from vertex v1, to vertex vk and, for any i and j such that 1 < i < j < k, let pij = vi, vi+1, . suppose that g is initialized by initialize-single-source(g, s) and then a sequence of relaxation steps that includes the call relax(u, v, w) is executed on the edges of g. the execution of a shortest-paths algorithm, however, the values need not indicate shortest paths. an edge-weighted digraph with nonnegative weights,Design an e log v algorithm for finding the shortest path from s to t where. to be precise, let g = (v, e) be a weighted, directed graph with weight function w : e r, and assume that g contains no negative-weight cycles reachable from the source vertex s v, so that shortest paths are well defined. an edge-weighted digraph with nonnegative weights and source s and sink t,Design an algorithm to find the shortest path from s to t that does not use.''' the function to find shortest paths from given vertex. since (s, vk) is a lower bound on the weight of any path from s to vk, we conclude that w(p) = (s, vk), and thus p is a shortest path from s to v = vk. now, assume that there is a path p'ij from vi to vj with weight w(p'ij) < w(pij). a critical path is a longest path through the dag, corresponding to the longest time to perform an ordered sequence of jobs. iterating through the edges in a, any path in the spt that starts. others, such as the bellman-ford algorithm, allow negative-weight edges in the input graph and produce a correct answer as long as no negative-weight cycles are reachable from the source. here again, we define the vertex set v, to be the set of vertices of g with non-nil predecessors, plus the source s:V = {v v : [v] nil} {s} ., but it contains shortest paths from the source defined in terms of edge weights instead of numbers of edges., the path to any reachable vertex can be found by starting at the vertex and following the π's back to the source. possible way is to enumerate all the routes from chicago to boston, add up the distances on each route, and select the shortest., which is a key to understanding the shortest-paths algorithms in this chapter., these lemmas apply to any sequence of relaxation steps, not just those that produce shortest-path values. shows two examples of relaxing an edge, one in which a shortest-path estimate decreases and one in which no estimate changes. is a negative cycle reachable from the source if and only if the queue. initialize distances to all vertices as infinite and distance to source as 0, then we find a topological sorting of the graph. moreover, relaxation is the only means by which shortest-path estimates and predecessors change. paths are not necessarily unique, and neither are shortest-paths trees. that if there is a negative cycle reachable from the source in.

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• ## Lecture 21: Single Source Shortest Paths - Bellman-Ford Algorithm

t is not reachable from s, there is no path at all,And therefore there is no shortest path from s to t. exists a shortest path from s to v in an edge-weighted digraph. g = (v, e) be a weighted, directed graph with source vertex s, and let g be initialized by initialize-single-source(g, s). first, knowing that a given problem can be cast as a polynomial-sized linear-programming problem immediately means that there is a polynomial-time algorithm for the problem. then for each vertex v v, there is a path from s to v if and only if bellman-ford terminates with d[v] < when it is run on g. (hint: how many distinct shortest-path estimates can there be in v - s at any point in time? in this section, we investigate a special case of linear programming that can be reduced to finding shortest paths from a single source. if the graph contains a negative-weight cycle reachable from the source, the bellman-ford algorithm detects its presence. the bellman-ford algorithm so that it sets d[v] to - for all vertices v for which there is a negative-weight cycle on some path from the source to v. prove the correctness of the bellman-ford algorithm, we start by showing that if there are no negative-weight cycles, the algorithm computes correct shortest-path weights for all vertices reachable from the source. a shortest path p from source s to vertex u can be decomposed into where y is the first vertex on the path that is not in v - s and x s immediately precedes y.// java program to find single source shortest paths in directed acyclic graphs. we have already seen how to solve this problem in the case where all the edges have the same weight (in which case the shortest path is simply the minimum number of edges) using bfs. without loss of generality that every vertex v v is reachable from a source vertex s v. let us define i(u, v) as the shortest-path weight from vertex u to vertex v using weight function wi. because y occurs before u on a shortest path from s to u and all edge weights are nonnegative (notably those on path p2), we have (s, y) (s, u), and thus. the total running time is therefore o((v + e) lg v), which is o(e lg v) if all vertices are reachable from the source.-destination shortest-paths problem: find a shortest path to a given destination vertex t from every vertex v. some instances of the single-source shortest-paths problem, there may be edges whose weights are negative. this chapter, we shall focus on the single-source shortest-paths problem: given a graph g = (v, e), we want to find a shortest path from a given source vertex s v to every vertex v v. the idea here is to prove by induction that a path exists from s to all vertices in v. shortest path from vertex u to vertex v is then defined as any path p with weight w (p) = (u, v). since the cycle e, f, e has weight 3 + (-6) = -3 < 0, however, there is no shortest path from s to e. (hint: the weight of the path from v to u along the cycle is -x., subpath p' is a shortest path from source s to vertex u. from s to v and the length of the shortest path from w to t. this chapter and in chapter 26, we show how to solve such problems efficiently. given these caveats, we know the shortest paths must be acyclic (with ≤ |v| distinct vertices) ⇒ ≤ |v| - 1 edges in each path. initialize distances to all vertices as infinite and distance to source as 0, then we find a topological sorting of the graph. following theorem shows that the dag-shortest-paths procedure correctly computes the shortest paths. solution: relax edges in ascending order and find a best path; then relax edges in. path p connects a vertex in s, namely s, to a vertex in v - s, namely u. the simplex algorithm finds an optimal solution to a linear programming problem by examining a sequence of points in the feasible region--the region in n-space that satisfies ax b. relaxing vertices in topological order, we can solve the single-source.// java program to find single source shortest paths in directed acyclic graphs. shall examine the shortest-path estimates on c just prior to the call relax (vk - 1, vk, w) and show that c is a negative-weight cycle, thereby contradicting the assumption that g contains no negative-weight cycles that are reachable from the source. we must prove that the three properties of shortest-paths trees hold for g. lawler [132] describes the linear-time algorithm for shortest paths in a dag, which he considers part of the folklore.

## Shortest Path in Directed Acyclic Graph - GeeksforGeeks

, along with the claim, implies that g is a shortest-paths tree. with this change,Computing path weights by multiplying edge weights in the original problem. we initialize the shortest-path estimates and predecessors by the following procedure. given a road map of the united states on which the distance between each pair of adjacent intersections is marked, how can we determine this shortest route? g = (v, e) be a weighted, directed graph with weight function w : e r and source vertex s v, and assume that g contains no negative-weight cycles that are reachable from s., following the edgeto[] array always yields a path back to s. claim that all vertices on cycle c are reachable from the source s. that every row in the matrix a of a linear program ax b corresponds to a difference constraint, a single-variable constraint of the form xi bk, or a single-variable constraint of the form -xi bk. let s v be the source vertex, and let g be initialized by initialize-single-source(g, s). for each vertex v v, we maintain an attribute d[v], which is an upper bound on the weight of a shortest path from source s to v. the representation we use for shortest paths is similar to the one we used for breadth-first trees in section 23.. a specific destination) - asymptotically this problem can be solved no faster than simply using single source shortest path algorithms to all the vertices.'s algorithm solves the single-source shortest-paths problem on a weighted, directed graph g = (v, e) for the case in which all edge weights are nonnegative. the shortest path from s to every other vertex; compute the shortest path. thus,The next lemma gives a simple but useful property of shortest-path weights. can be used to print a shortest path from s to v. other problems that can be cast as linear programming include the single-pair shortest-path problem (exercise 25. then, pij is a shortest path from vi to vj. dijkstra's algorithm, the bellman-ford algorithm uses the technique of relaxation, progressively decreasing an estimate d[v] on the weight of a shortest path from the source s to each vertex v v until it achieves the actual shortest-path weight (s, v). formulate the arbitrage problem as a negative-cycle detection problem,Replace each weight by its logarithm, negated. bellman-ford be run on a weighted, directed graph g = (v, e) with source s and weight function w : e r. there are infinitely many paths from s to c: s, c, s, c, d, c, s, c, d, c, d, c, and so on. because the cycle c, d, c has weight 6 + (-3) = 3 > 0, the shortest path from s to c is s, c, with weight (s, c) = 5. a general weighted graph, we can calculate single source shortest distances in o(ve) time using bellman–ford algorithm. following lemma is crucial to proving the correctness of the shortest-paths algorithms that appear later in this chapter. algorithm in this chapter calls initialize-single-source and then repeatedly relaxes edges. we run dijkstra's algorithm on a weighted, directed graph g = (v, e) with nonnegative weight function w and source s, then at termination, the predecessor subgraph g is a shortest-paths tree rooted at s. graph in linear time and can return the distance of the shortest path. vertex 5 as the source (setting its distance to 0), we initialize all the other distances to ∞. understand single-source shortest-paths algorithms, it is helpful to understand the techniques that they use and the properties of shortest paths that they exploit. showing that a path in g from source s to vertex v is unique. example, consider problem of finding the 5-vector x = (xi) that satisfies. far, we have shown that relaxation causes the shortest-path estimates to descend monotonically toward the actual shortest-path weights. we run dijkstra's algorithm on a weighted, directed graph g = (v, e) with nonnegative weight function w and source s, then at termination, d[u] = (s, u) for all vertices u v. (b) the shaded edges form a shortest-paths tree rooted at the source s. that in a weighted, directed graph g = (v, e) with weight function w : e r, no path connects a source vertex s v to a given vertex v v. g = (v, e) be a weighted, directed graph with source vertex s and weight function w : e r. 3: edge (u2,u1) relaxes (since a shorter path to vertex 2 was found in the previous iteration) updating the distance to 1.
• ## CHAPTER 25: SINGLE-SOURCE SHORTEST PATHS

then, the predecessor subgraph g is a shortest-paths tree rooted at s.) vertex u, therefore, has the smallest shortest-path estimate of any vertex in v - s. an example of a weighted, directed graph g = (v, e) with weight function w: e r and source s such that g satisfies the following property: for every edge (u, v) e, there is a shortest-paths tree rooted at s that contains (u, v) and another shortest-paths tree rooted at s that does not contain (u, v). shortest-paths algorithms, such as dijkstra's algorithm, assume that all edge weights in the input graph are nonnegative, as in the road-map example. modify the dag-shortest-paths procedure so that it finds a longest path in a directed acyclic graph with weighted vertices in linear time. on a first reading, you may also wish to ignore completely the lemmas concerning predecessor subgraphs and shortest-paths trees (lemmas 25. , vi be the subpath of p from vertex vi, to vertex vj.= float("inf") else "inf" ,Print ("following are shortest distances from source %d " % s). if there is no path from s to v with exactly k edges, then k(s, v) = . a given source vertex s, find the minimum weight paths to every vertex reachable from s denoted. they are often used to represent time, cost, penalties, lossage, or any other quantity that accumulates linearly along a path and that one wishes to minimize.), we obtain the sum of the shortest-path estimates around cycle c:Since each vertex in the cycle c appears exactly once in each summation.// c++ program to find single source shortest paths for directed acyclic graphs. because vertex g is reachable from a vertex whose shortest-path weight is -, it, too, has a shortest-path weight of -. , 5, such that the following 8 difference constraints are satisfied:One solution to this problem is x = (-5, -3, 0, -1, -4), as can be verified directly by checking each inequality. prove that for every vertex v v, there exists a path from s to v in g and that this property is maintained as an invariant over any sequence of relaxations. this procedure (with a few slight modifications) is useful for finding critical paths for pert charts. because there are edges from the source vertex v0 to all other vertices in the constraint graph, any negative-weight cycle in the constraint graph is reachable from v0. then, the weight of a shortest path from s to v is (s, v) = (s, u) + w(u, v). (a) a weighted, directed graph with shortest-path weights from source s. may be multiple paths of the lowest weight from one vertex to another;. let us call initialize-single-source(g, s) and then execute any sequence of relaxation steps on edges of g that produces d[v] = (s, v) for all v v. , vk be a shortest path from s to v, where v0 = s and vk = v. if there is a path from vertex u to vertex v, then u precedes v in the topological sort. a weighted directed acyclic graph and a source vertex in the graph, find the shortest paths from given source to all other vertices., compute the length of the shortest path from s to v for each. the weight of a critical path is a lower bound on the total time to perform all the jobs. the shortest-paths algorithms in this chapter set the attributes so that the chain of predecessors originating at a vertex v runs backwards along a shortest path from s to v. a weighted, directed graph g = (v, e) has source vertex s and no cycles, then at the termination of the dag-shortest-paths procedure, d[v] = (s, v) for all vertices v v, and the predecessor subgraph g is a shortest-paths tree. a general weighted graph, we can calculate single source shortest distances in o(ve) time using bellman–ford algorithm. begins by proving some important properties of shortest paths in general and then proves some important facts about relaxation-based algorithms. shows a weighted, directed graph and two shortest-paths trees with the same root. vertices such as h, i, and j are not reachable from s, and so their shortest-path weights are , even though they lie on a negative-weight cycle. analogously, there are infinitely many paths from s to e: s, e, s, e, f, e, s, e, f, e, f, e, and so on. the execution of the algorithm for shortest paths in a directed acyclic graph. then, is a path from v1 to vk whose weight w(p1i) + w(p'ij) + w(pjk) is less than w(p), which contradicts the premise that p is a shortest path from v1 to vk. we can calculate single source shortest distances in o(v+e) time for dags. the main technique used by the algorithms in this chapter is relaxation, a method that repeatedly decreases an upper bound on the actual shortest-path weight of each vertex until the upper bound equals the shortest-path weight.
• ### Shortest Paths

(here, the shortest path is by weight, not the number of edges. , vk, where v0 = vk, that is reachable from the source s. problem is equivalent to finding the unknowns xi, for i = 1, 2, . the general linear-programming problem, we are given an m n matrix a, an m-vector b, and an n-vector c. for a given source vertex s, the scaling algorithm first computes the shortest-path weights 1(s, v) for all v v, then computes 2(s, v) for all v v, and so on, until it computes k(s, v) for all v v. suppose that a shortest path p from a source s to a vertex v can be decomposed into for some vertex u and path p'. if we solve the single-source problem with source vertex u, we solve this problem also. at s such that every tree path is a shortest path in the digraph. if the graph g = (v, e) contains no negative-weight cycles reachable from the source s, then for all v v, the shortest-path weight (s, v) remains well defined, even if it has a negative value. there must be some path from s to u, for otherwise d[u] = (s, u) = by corollary 25. solution cannot have any positive weight cycles (since the cycle could simply be removed giving a lower weight path). interesting application of this algorithm arises in determining critical paths in pert chart2 analysis. can now show that if, after we have performed a sequence of relaxation steps, all vertices have been assigned their true shortest-path weights, then the predecessor subgraph g is a shortest-paths tree. shortestpath(self, s):# mark all the vertices as not visited. then, lines 7-8 relax each edge (u, v) leaving u, thus updating the estimate d[v] and the predecessor [v] if the shortest path to v can be improved by going through u. problem can be solved by formulating it as a longest paths problem. algorithms ignore zero-weight edges that form cycles,So that the shortest paths they find have no cycles. we shall focus our attention on the situation at the beginning of the iteration of the while loop in which u is inserted into s and derive the contradiction that d[u] = (s, u) at that time by examining a shortest path from s to u. motorist wishes to find the shortest possible route from chicago to boston. shortest paths are always well defined in a dag, since even if there are negative-weight edges, no negative-weight cycles can exist. variants of simplex remain the most popular method for solving linear-programming problems. e start with the following lemma, which shows that the predecessor subgraph always forms a rooted tree whose root is the source. the general linear-programming problem, we wish to optimize a linear function subject to a set of linear inequalities. that for the edges on the shortest paths the relaxation criteria gives equalities. initially, the only vertex in g is the source vertex, and the lemma is trivially true. path is monotonic if the weight of every edge on the path is either. because vertices e and f form a negative-weight cycle reachable from s, they have shortest-path weights of -. most of the lemmas describe the outcome of executing a sequence of relaxation steps on the edges of a weighted, directed graph that has been initialized by initialize-single-source. the shortest-path estimates are shown within the vertices, and shaded edges indicate predecessor values: if edge (u,v) is shaded, then [v] = u.-pairs shortest-paths problem: find a shortest path from u to v for every pair of vertices u and v. it gives sufficient conditions for relaxation to cause a shortest-path estimate to converge to a shortest-parth weight. an edge-weighted digraph and a designated vertex s,A shortest-paths tree (spt) is a subgraph containing s. following theorem shows that a solution to a system of difference constraints can be obtained by finding shortest-path weights in the corresponding constraint graph. we would also like to show that once a sequence of relaxations has computed the actual shortest-path weights, the predecessor subgraph g induced by the resulting values is a shortest-paths ree for g . bellman-ford algorithm solves the single-source shortest-paths problem in the more general case in which edge weights can be negative. thus, given a vertex v for which [v] nil, the procedure print-path (g, s, v) from section 23. this problem can be solved by running a single-source algorithm once from each vertex; but it can usually be solved faster, and its structure is of interest in its own right. we conclude that there exists a unique simple path in g from s to v, and thus g forms a rooted tree with root s.
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