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Exercises 24.4-1


Find a feasible solution or determine that no feasible solution exists for the following system of difference constraints:

x1 -x2 ≤1

x1 -x4 ≤-4

x2 -x3 ≤2

x2 -x5 ≤7

x2 -x6 ≤5

x3 -x6 ≤10

x4 -x2 ≤2

x5 -x1 ≤-1

x5 -x4 ≤3

x6 -x3 ≤-8

Answer

(-5, -3, 0, -1, -6, -8)

Exercises 24.4-2


Find a feasible solution or determine that no feasible solution exists for the following system of difference constraints:

x1 -x2 ≤4

x1 -x5 ≤5

x2 -x4 ≤-6

x3 -x2 ≤1

x4 -x1 ≤3

x4 -x3 ≤5

x4 -x5 ≤10

x5 -x3 ≤-4

x5 -x4 ≤-8

Answer

没有解,因为x4 -> x2 -> x3 -> x5 -> x1 -> x4形成了一个负权回路.

Exercises 24.4-3


Can any shortest-path weight from the new vertex v0 in a constraint graph be positive? Explain.

Answer

不可能为正数,因为最大已经是0了.不可能大于0.

Exercises 24.4-4


Express the single-pair shortest-path problem as a linear program.

Answer

将G视为约束图,得到差分约束Ax<=b。设起点为i,终点为j,求解目标为 (xj - xi) 的最大值。

Exercises 24.4-5


Show how to modify the Bellman-Ford algorithm slightly so that when it is used to solve a system of difference constraints with m inequalities on n unknowns, the running time is O(nm).

Answer

V0这个顶点和他的n条权值为0的边其实是没有意义的,一开始初始化的时候可以对所有的点v,令d[v] = 0.

Exercises 24.4-6


Suppose that in addition to a system of difference constraints, we want to handle equality constraints of the form xi = xj + bk. Show how the Bellman-Ford algorithm can be adapted to solve this variety of constraint system.

Answer

xi >= xj + bk

xi <= xj + bk

Exercises 24.4-7


Show how a system of difference constraints can be solved by a Bellman-Ford-like algorithm that runs on a constraint graph without the extra vertex v0.

Answer

同练习24.4-5

Exercises 24.4-8 *


Let Ax ≤ b be a system of m difference constraints in n unknowns. Show that the Bellman- Ford algorithm, when run on the corresponding constraint graph, maximizes x1+x2+...+xn subject to Ax≤b and xi ≤0 for all xi.

Answer

Bellman-Ford 处理线性规划问题的算法:添加虚拟的顶点v0到约束图,并设置该节点到其他节点的边权重均为0,之后以 v0 为源使用 Bellman-Ford 算法,得出的 v0 到各点的最小距离即为满足条件的解 xi,此结果已经最大化了x1+x2+...xn. 正确性证明:(反证法) 假设存在另一组解 (y1,y2...yn),其和更大,则至少存在一个 yi>xi。由于xi对应了约束图上 v0 顶点到 vi 顶点的最小距离,我们设该最小路径通过的顶点分别是 v1,v2...vi,我们有: 不等式1: (根据 y 是满足约束条件的一组解) y2-y1 < w(v1,v2), y3-y2 < w(v2,v3) ... yi-yi-1 < w(vi-1,vi) 不等式2: (根据 y1<0 的约束) yi < yi-y1 等式: (根据 v1 到 vi 是求出的最短路径) xi = w(v0,v1)+w(v1,v2)+...+w(v(i-1),vi) 注意该等式右边第一项是0. 结合以上三个式子,我们有: yi < yi-y1 < yi-y_(i-1)+y_(i-1)...-y2+y2-y1 < w(v1,v2)+w(v2,v3)+...+w(v_(i-1),v_i) = xi 与假设矛盾,因此 x 就是和最大的解,证毕。

Exercises 24.4-9 *


Show that the Bellman-Ford algorithm, when run on the constraint graph for a system Ax ≤ b of difference constraints, minimizes the quantity (max {xi} - min {xi}) subject to Ax ≤ b. Explain how this fact might come in handy if the algorithm is used to schedule construction jobs.

Answer

根据上题,Bellman-Ford 算法在约束图上给出的解为 x1,x2...x3。设其中的最小值为 xi。依旧考虑 v0 到 vi 的这一情况下的最短路径,记路过的顶点为 v1, v2... vi-1。首先由于 w(v0,v1)=0, 则 x1=0。(因为最短路径的子路径仍旧是最短路径)。而由于距 v0 的最短距离不可能为正数,则 x1 即为解 x 中的最大值。此时我们有数据区间 x1-xi=-xi=-[w(v1,v2)+w(v2,v3)+...w(vi-1,vi)]。 考虑存在另一组满足约束的解 y1,y2...yi。那么 y1-yi = -(y2-y1)-(y3-y2)-...-(yi-yi-1) > -w(v1,v2)-w(v2,v3)-...-w(vi-1,vi) = -xi。解 y 的极差一定不小于 y1-yi,而该值大于原解 x。由此得证 x 已经是满足要求的极差最小的解。 考虑每个 x 是一个任务完成的时刻,在一定约束下,我们总是希望完成任务的总时长最小,此即为最小化极差的应用。

Exercises 24.4-10


Suppose 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. Show how to adapt the Bellman-Ford algorithm to solve this variety of constraint system.

Answer

新造一个节点u,令约束条件变成xi - xu ≤ bk.

并且初始化d(u) = 0.

Exercises 24.4-11


Give an efficient algorithm to solve a system Ax ≤ b of difference constraints when all of the elements of b are real-valued and all of the unknowns xi must be integers.

Answer

把所有的b向下取整。因为 xi - xj ≤ b 同时 xi - xj 是整数,可得 xi - xj 一定小于等于b向下取整。

Exercises 24.4-12 *


Give an efficient algorithm to solve a system Ax ≤ b of difference constraints when all of the elements of b are real-valued and a specified subset of some, but not necessarily all, of the unknowns xi must be integers.

Answer

仍然采用最短路径算法,在松弛过程中,若有必要(x.v为整数),取 v.d = {(u.d + w(u, v)) 向下取整}。 证明: 假设这个算法得出的解(最短路径)不满足约束条件,更具体的,xi - xj > bm。 因为我们在松弛过程中,由于向下取整,导致实际取的 v.d ≤ u.d + w(u, v),而如果取 v.d = u.d + w(u, v) 结果是满足约束条件的,可知一定是j点得最短路径取小了。 另一方面,原来的约束 xi - xj ≤ bm 在约束图中是点j到点i的路径。如果i点在j点之前已经取得最短路径,对j点的循环过程会导致i点最短路径更短,可知i点一定在j点之后取得最短路径。根据松弛过程可知,算出的i点的最短路径一定满足约束,即 xi - xj ≤ bm。 与假设矛盾。 因此,按照该方法算出的结果一定是一个可行解。


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