You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We study a classical iterative algorithm for balancing matrices in the @math norm via a scaling transformation. This algorithm, which goes back to Osborne and Parlett & Reinsch in the 1960s, is implemented as a standard preconditioner in many numerical linear algebra packages. Surprisingly, despite its widespread use over several decades, no bounds were known on its rate of convergence. In this paper we prove that, for any irreducible @math (real or complex) input matrix @math , a natural variant of the algorithm converges in @math elementary balancing operations, where @math measures the initial imbalance of @math and @math is the target imbalance of the output matrix. (The imbalance of @math is @math , where @math are the maximum entries in magnitude in the @math th row and column respectively.) This bound is tight up to the @math factor. A balancing operation scales the @math th row and column so that their maximum entries are equal, and requires @math arithmetic operations on average, where @math is the number of non-zero elements in @math . Thus the running time of the iterative algorithm is @math . This is the first time bound of any kind on any variant of the Osborne-Parlett-Reinsch algorithm. We also prove a conjecture of Chen that characterizes those matrices for which the limit of the balancing process is independent of the order in which balancing operations are performed.

Cited Abstract(s):
[1]: 

[2]: An n × n nonnegative matrix A is said to be (doubly stochastic) scalable if there exist two positive diagonal matrices X and Y such that XAY is doubly stochastic. We derive an upper bound on the norms of the scaling factors X and Y and give a polynomial-time complexity bound on the problem of computing the scaling factors to a prescribed accuracy.

[3]: Abstract Elementary proofs are given for theorems of Bapat and Raghavan on the scaling of nonnegative multidimensional matrices. Theorems of Sinkhorn and of Brualdi, Parter, and Schneider are derived as corollaries. For positive two-dimensional matrices, Hilbert's projective metric and a theorem of G. Birkhoff are used to prove that Sinkhorn's original iterative procedure converges geometrically; the ratio of convergence is estimated from the given data.

[4]: 

[5]: Given an n × m nonnegative matrix A = (a ij ) and positive integral vectors r e ℜ n and c e Âm having a common one-norm h, the (r,c)-scaling problem is to obtain positive diagonal matrices X and Y, if they exist, such that XAY has row and column sums equal to r and c, respectively. The entropy minimization problem corresponding to A is to find an n × m matrix z = (z ij ) having the same zero pattern as A, the sum of whose entries is a given number h, its row and column sums are within given integral vectors of lower and upper bounds, and such that the entropy function consisting of the sum of the terms z ij ln (z ij a ij ) is minimized. When the lower and upper bounds coincide, matrix scaling and entropy minimization are closely related. In this paper we present several complexity bounds for the e-approximate (r,c)-scaling problem, polynomial in n,m,h, 1 e, and ln V v, where V and v are the largest and the smallest positive entries of A, respectively. These bounds, although not polynomial in ln(1 1 e), not only provide alternative complexities for the polynomial time algorithms, but could result in better overall complexities. In particular, our theoretical analysis supports the practicality of the well-known RAS algorithm. In our analysis we obtain bounds on the norm of scaling vectors which will be used in deriving not only some of the above complexities, but also a complexity for square nonnegative matrices having positive permanent. In particular, our results extend, nontrivially, many bounds for the doubly stochastic scaling of square nonnegative matrices previously given by Kalantari and Khachiyan to the case of general (r,c)-scaling. Finally, we study a more general entropy minimization problem where row and column sums are constrained to lie in prescribed intervals, and the sum of all entries is also prescribed. Balinski and Demange described an RAS type algorithm for its continuous version, but did not analyze its complexity. We show that this algorithm produces an 1 e-approximate solution within complexity polynomial in n, m, h, V v and 1 1 e.

[6]: We present a deterministic strongly polynomial algorithm that computes the permanent of a nonnegative n × n matrix to within a multiplicative factor of en. To this end we develop the first strongly polynomial-time algorithm for matrix scaling an important nonlinear optimization problem with many applications. Our work suggests a simple new (slow) polynomial time decision algorithm for bipartite perfect matching, conceptually different from classical approaches. ∗Hebrew University. Work supported in part by a grant of the Binational Israel-US Science Foundation. †Hebrew University ‡Hebrew University. Work partially supported by grant 032-7736 from the Israel Academy of Sciences. Part of this work was done during a visit to the Institute for Advanced Study, under the support of a Sloan Foundation grant 96-6-2.
A different notion of matrix balancing is often known as Sinkhorn balancing after Sinkhorn [1] , who proposed a natural iterative algorithm analogous to that of Osborne-Parlett-Reinsch. The goal here is to find a scaling matrix @math such that @math has prescribed row and column sums. A polynomial time algorithm for this problem was given by Kalantari and Khachian [2] , while the convergence rate of Sinkhorn's iterative algorithm was studied by several authors [3] [4] [3]7 [3]6 . In particular Linial, Samorodnitsky and Wigderson [3]7 gave strongly polynomial bounds (after a non-Sinkhorn preprocessing step) and derived a surprising approximation scheme for the permanent of a non-negative matrix.