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A primal-dual interior point method whose running time depends only on the constraint matrix. (English) Zbl 0868.90081
Summary: We propose a primal-dual “layered-step” interior point (LIP) algorithm for linear programming with data given by real numbers. This algorithm follows the central path, either with short stops or with a new type of step called a “layered least squares” (LLS) step. The algorithm returns an exact optimum after a finite number of steps – in particular, after \(O(n^{3.5}c(A))\) iterations, where \(c(A)\) is a function of the coefficient matrix. The LLS steps can be thought of as accelerating a classical path-following interior point method. One consequence of the new method is a new characterization of the central path: we show that it composed of at most \(n^2\) alternating straight and curved segments. If the LIP algorithm is applied to integer data, we get as another corollary a new proof of a well-known theorem by Tardos that linear programming can be solved in strongly polynomial time provided that \(A\) contains small-integer entries.

MSC:
90C51 Interior-point methods
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