Abstract Generated abstract
The paper studies feasibility conditions for multi-index linear programming problems, including transportation-type problems with equality and inequality constraint blocks. It develops reduction and “cutting” arguments showing how feasible solutions of lower-dimensional subproblems can be embedded into the original problem, and how convex combinations of such cut solutions can satisfy an additional block of constraints under compatibility assumptions. For transportation-type problems, the paper gives sufficient, and in a stated class necessary and sufficient, consistency conditions for existence of feasible solutions, relating them to earlier results on coalition games. It also considers problems with nonnegative coefficients and prescribed lower and upper bounds, deriving iterative necessary conditions for feasibility and inequalities involving balanced bounds for the transportation case.
Full Text
Reports of the Academy of Sciences of the USSR
1964, Volume 158, No. 4
MATHEMATICS
B. S. VERKHOVSKII
ON THE EXISTENCE OF A SOLUTION OF A MULTI-INDEX LINEAR PROGRAMMING PROBLEM
(Presented by Academician A. A. Dorodnitsyn on 25 IV 1964)
All unexplained notation is to be found in \((^{1})\).
Consider the problem of minimizing the functional
\[ L=\sum_{k\in M} p_{i_1\ldots i_s}x_{i_1\ldots i_s} \tag{1} \]
subject to the conditions
\[ \sum_{k\in M_j} a^{(j)}_{i_1\ldots i_s}x_{i_1\ldots i_s} \begin{cases} \leqslant b_{M_j(i_1\ldots i_s)},\\ = b_{M_j(i_1\ldots i_s)}; \end{cases} \tag{2} \]
\[ x_{i_1\ldots i_s}\geqslant 0;\qquad j=1,\ldots,t;\qquad i_k=1,\ldots,n_k;\qquad k=1,\ldots,s. \tag{3} \]
All \(a^{(j)}_{i_1\ldots i_s}\) and \(p_{i_1\ldots i_s}\) are prescribed real numbers. In the particular case when all \(a^{(j)}_{i_1\ldots i_s}=1\), we obtain a multi-index linear programming problem of transportation type. In addition, all \(b_{M_j(i_1\ldots i_s)}\geqslant 0\) are given.
We shall call the problem (1)—(3) an \(X\)-problem. The totality of all constraints for a fixed \(j\) will be called the \(j\)-block of constraints.
Let there exist \(\widetilde M\ne\varnothing\) such that, if \(\widetilde M\cap M_j\ne \widetilde M\), then all constraints entering the \(j\)-block of constraints are given in the form of inequalities. For definiteness, let \(\widetilde M=\{r+1,\ldots,s\}\).
Consider
\[ \prod_{k=r+1}^{s} n_k \]
independent problems:
\[ \sum_{k\in M_j\setminus \widetilde M} a^{(j)}_{i_1\ldots i_s}y_{i_1\ldots i_r} \begin{cases} \leqslant b_{M_j(i_1\ldots i_s)}\\ = b_{M_j(i_1\ldots i_s)} . \end{cases} \quad \text{for all } j \text{ such that } M_j\cap \widetilde M=\widetilde M; \tag{4} \]
\[ \sum_{k\in M_j\setminus \widetilde M\cap M_j} a^{(j)}_{i_1\ldots i_s}y_{i_1\ldots i_r} \leqslant b_{M_j(i_1\ldots i_s)} \quad \text{for all } j \text{ such that } M_j\cap \widetilde M\ne \widetilde M; \tag{5} \]
\[ y_{i_1\ldots i_r}\geqslant 0. \tag{6} \]
In (4) and (5) all indices \(i_{r+1},\ldots,i_s\) are fixed.
Theorem 1. Suppose that for at least one set of values of the indices \(i_{r+1}=\omega_{r+1},\ldots,i_s=\omega_s\) there exists a feasible solution of the problem (4)—(6). All \(1\leqslant \omega_k\leqslant n_k,\ k=r+1,\ldots,s\). Then the \(X\)-problem also has a feasible solution.
Proof. By direct substitution it is easy to verify that
\[ x_{i_1\ldots i_s}= \begin{cases} y_{i_1\ldots i_r}, & \text{if } \displaystyle\bigcap_{k\in \widetilde M} p(i_k=\omega_k)=1,\\[6pt] 0, & \text{if } \displaystyle\bigcap_{k\in \widetilde M} p(i_k=\omega_k)=0, \end{cases} \]
is a feasible solution of the \(X\)-problem. Here \(p(i_k=\omega_k)\) is a logical condition,
\[ p(i_k=\omega_k)= \begin{cases} 1, & \text{if } i_k=\omega_k,\\ 0, & \text{if } i_k\ne \omega_k. \end{cases} \]
We shall say that the \(X\)-problem is cut on the set of indices \(\widetilde M\) with respect to the tuple \((\omega_{r+1},\ldots,\omega_s)\).
Consider the \(X(t)\)-problem, which differs from the \(X\)-problem in that it does not include the \(t\)-block constraints.
Theorem 2. Suppose:
1) the \(X(t)\)-problem is cut on the set of indices
\[ \widetilde M \equiv \bigcup_{j=1}^{t-1} M_j \setminus M_t \]
for all tuples \((\omega_{r+1},\ldots,\omega_s)\);
2) there exists at least one \(j_* \ne t\) such that all \(a^{(j_*)}_{i_1\ldots i_s}=a^{(t)}_{i_1\ldots i_s}\);
3) the \(j_*\)-block constraints and the \(t\)-block constraints are given in the form of equalities;
4) the compatibility conditions are satisfied,
\[ \sum_{k\in M_{j_*}\cup M_t\setminus M_{j_*}} b_{M_{j_*}(i_1\ldots i_s)} = \sum_{k\in M_{j_*}\cup M_t\setminus M_t} b_{M_t(i_1\ldots i_s)} . \]
Then the \(X\)-problem has a feasible solution.
Proof. Every solution of the \(X(t)\)-problem can be written in the form
\[ x^{(\omega_{r+1}\ldots\omega_s)}_{i_1\ldots i_s} = \begin{cases} y_{i_1\ldots i_r}, & \text{if } \displaystyle\bigcap_{k\in \widetilde M} p(i_k=\omega_k)=1,\\ 0, & \text{if } \displaystyle\bigcap_{k\in \widetilde M} p(i_k=\omega_k)=0. \end{cases} \]
We shall show that there exist such
\[ \lambda_{\omega_{r+1}\ldots\omega_s}\ge 0,\qquad \sum_{\omega_{r+1}=1}^{n_{r+1}}\cdots \sum_{\omega_s=1}^{n_s} \lambda_{\omega_{r+1}\ldots\omega_s}=1, \tag{7} \]
for which the convex combination
\[ \sum_{\omega_{r+1}=1}^{n_{r+1}}\cdots \sum_{\omega_s=1}^{n_s} x^{(\omega_{r+1}\ldots\omega_s)}_{i_1\ldots i_s} \lambda_{\omega_{r+1}\ldots\omega_s} = \bar x_{i_1\ldots i_s} \]
satisfies the \(t\)-block constraints
\[ \sum_{k\in M_t} a^{(t)}_{i_1\ldots i_s}\bar x_{i_1\ldots i_s} = \sum_{k\in M_t} a^{(t)}_{i_1\ldots i_s} \left( \sum_{\omega_{r+1}=1}^{n_{r+1}}\cdots \sum_{\omega_s=1}^{n_s} x^{(\omega_{r+1}\ldots\omega_s)}_{i_1\ldots i_s} \lambda_{\omega_{r+1}\ldots\omega_s} \right) = \]
\[ = \sum_{k\in M_t} a^{(t)}_{i_1\ldots i_s} x^{(i_{r+1}\ldots i_s)}_{i_1\ldots i_s} \lambda_{i_{r+1}\ldots i_s} = \sum_{k\in M_t} a^{(t)}_{i_1\ldots i_s} y_{i_1\ldots i_r}\lambda_{i_{r+1}\ldots i_s} = b_{M_t(i_1\ldots i_s)} . \]
Since, by definition, \(\widetilde M\cap M_t=\varnothing\), it follows that
\[ \lambda_{i_{r+1}\ldots i_s} = \frac{b_{M_t(i_1\ldots i_s)}} {\displaystyle\sum_{k\in M_t} a^{(t)}_{i_1\ldots i_s}y_{i_1\ldots i_r}} . \]
It remains to show that condition (7) is satisfied. From the compatibility conditions,
\[ \sum_{k\in M_{j_*}\cup M_t\setminus M_t} b_{M_t(i_1\ldots i_s)} = \sum_{k\in M_{j_*}\cup M_t} \left( \sum_{\omega_{r+1}=1}^{n_{r+1}}\cdots \sum_{\omega_s=1}^{n_s} a^{(t)}_{i_1\ldots i_s} x^{(\omega_{r+1}\ldots\omega_s)}_{i_1\ldots i_s} \lambda_{\omega_{r+1}\ldots\omega_s} \right) = <!-- source-page: 003 --> \[ = \left( \sum_{\omega_{r+1}=1}^{n_{r+1}} \cdots \sum_{\omega_s=1}^{n_s} \lambda_{\omega_{r+1}\ldots\omega_s} \right) \left( \sum_{k \in M_{j_*}\cup M_t} u_{i_1\ldots i_s}^{(j_*)} x_{i_1\ldots i_s}^{(\omega_{r+1}\ldots\omega_s)} \right) = \]
\[ = \left( \sum_{\omega_{r+1}=1}^{n_{r+1}} \cdots \sum_{\omega_s=1}^{n_s} \lambda_{\omega_{r+1}\ldots\omega_s} \right) \left( \sum_{k \in M_{j_*}\cup M_t \setminus M_{j_*}} b_{M_{j_*}(i_1\ldots i_s)} \right) \]
it follows that
\[ \sum_{\omega_{r+1}=1}^{n_{r+1}} \cdots \sum_{\omega_s=1}^{n_s} \lambda_{\omega_{r+1}\ldots\omega_s} = 1. \]
The theorem is proved.
Consider a multi-index linear programming problem of transportation type, in which all constraints are given in the form of equalities, i.e., conditions (2) are written as follows:
\[ \sum_{k \in M_j} x_{i_1\ldots i_s} = b_{M_j(i_1\ldots i_s)}. \tag{2′} \]
Denote \(J_0 \equiv \{1,\ldots,t\}\), \(M_j^{(0)} \equiv M_j\). For each \(r=1,\ldots,t-2\), consider
\[ M_j^{(r)} \equiv M_j^{(r-1)} \setminus \bigcap_{j \in J_{r-1}} M_j^{(r-1)} \quad \text{for all } j \in J_{r-1}, \]
\[ M^{(r)} \equiv \bigcup_{j \in J_{r-1}} M_j^{(r-1)}. \]
Theorem 3. If for each \(r=1,\ldots,t-2\) there exists, in the last resort, one \(j_r \in J_{r-1}\) such that
\[ M^{(r)} \setminus M_{j_r}^{(r)} \subset \bigcap_{j \in J_r} M_j^{(r)}, \quad \text{where } J_r = J_{r-1}\setminus j_r, \]
then the multi-index problem of transportation type has a feasible solution if and only if the consistency conditions are satisfied.
Sufficiency. For \(t=2\), the consistency conditions are sufficient for any \(M_1\) and \(M_2\). In this case the feasible solution is written as follows:
\[ x_{i_1\ldots i_s} = \frac{ b_{M_1(i_1\ldots i_s)}\, b_{M_2(i_1\ldots i_s)} }{ \sum\limits_{k \in M_2^{(1)}} b_{M_1(i_1\ldots i_s)} }. \]
Consider the case \(t=3\). There exists \(j_1\) such that
\[ M^{(1)} \setminus M_{j_1}^{(1)} \subset \bigcap_{j \in J_1} M_j^{(1)}. \tag{8} \]
It is not hard to see that \(J_r\) contains \(t-r\) elements, i.e., for \(t=3\), \(J_1\) contains two elements.
Since for the \(X(j_1)\)-problem all the conditions of Theorem 2 are satisfied, for \(t=3\) the problem (1), (2′), (3) has a feasible solution.
Suppose that the theorem is true for \(t=\tau\). We shall show that it is true for \(t=\tau+1\). In this case condition (8) is also satisfied, and \(J_1\) contains \(\tau\) elements. Since the \(X(j_1)\)-problem has a solution, the \(X\)-problem also has a solution by Theorem 2. Necessary and sufficient conditions for the existence of a feasible solution to problem (2′), (3) were obtained in [2] by another method, in considering mixed strategies in coalition games.
Consider the case when all \(a_{i_1\ldots i_s}^{(j)} \ge 0\) and upper and lower bounds are imposed on all \(x_{i_1\ldots i_s}\), i.e.,
\[ 0 \le m_{i_1\ldots i_s}^{(1)} \le x_{i_1\ldots i_s} \le m_{i_1\ldots i_s}^{(0)} \le \infty. \tag{3′} \]
Denote
\[ \sum_{k\in M_j} m^{(l)}_{i_1\ldots i_s}x_{i_1\ldots i_s}\equiv A^{(l,l)}_{M_j(i_1\ldots i_s)} . \]
We shall compute
\[ m^{(l)}_{i_1\ldots i_s} = (-1)^l \min_{1\le j\le t} \left[ (-1)^l \left( \frac{b_{M_j(i_1\ldots i_s)}-A^{(j,l-1)}_{M_j(i_1\ldots i_s)}}{a^{(j)}_{i_1\ldots i_s}} + m^{(l-1)}_{i_1\ldots i_s} \right), \, (-1)^l m^{(l-2)}_{i_1\ldots i_s} \right]. \]
Theorem 4. In order that problem (2), (3′) have a solution, it is necessary that the condition
\[ (-1)^l\left[m^{(l)}_{i_1\ldots i_s}-m^{(l-1)}_{i_1\ldots i_s}\right]\ge 0 \]
be satisfied for all \(l=2,3,\ldots;\ i_k=1,\ldots,n_k;\ k=1,\ldots,s\). Moreover, after a finite number of steps \(l\), either all
\[ m^{(l)}_{i_1\ldots i_s}=m^{(l-2)}_{i_1\ldots i_s}, \]
and in this case all lower and upper bounds are balanced, or at least in one cell the condition
\[ (-1)^l\left[m^{(l)}_{i_1\ldots i_s}-m^{(l-1)}_{i_1\ldots i_s}\right]\ge 0 \]
is violated, i.e. problem (2), (3′) has no feasible solution.
Consider the multi-index LP problem of transportation type.
Theorem 5. If \(\mu_{i_1\ldots i_s}\) and \(M_{i_1\ldots i_s}\) are, respectively, the values of the balanced lower and upper bounds, then the inequalities
\[ \sum_{k\in M_j} M_{i_1\ldots i_s} + \left(\prod_{l\in M_j} n_l-1\right) \sum_{k\in M_j}\mu_{i_1\ldots i_s} \le \left(\prod_{l\in M_j} n_l\right)b_{M_j(i_1\ldots i_s)} \le \]
\[ \le \sum_{k\in M_j}\mu_{i_1\ldots i_s} + \left(\prod_{l\in M_j} n_l-1\right) \sum_{k\in M_j} M_{i_1\ldots i_s} \qquad \text{for all } j=1,\ldots,t. \]
Special cases of Theorems 4 and 5 were considered by the author in work \((^3)\).
Central Economic-Mathematical Institute
Academy of Sciences of the USSR
Received
23 IV 1964
CITED LITERATURE
- G. S. Verkhovskii, DAN, 151, No. 3 (1963).
- N. N. Vorob’ev, Theory of Probability and Its Applications, 7, 2 (1962).
- G. S. Verkhovskii, On an Algorithm for Solving a Multi-Index Transportation Problem on an Electronic Computer, Novosibirsk, 1962.