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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 50 (1991), S. 331-342 
    ISSN: 1436-4646
    Keywords: Potential reduction algorithm ; linear complementarity problem ; interior point algorithm ; Karmarkar's algorithm ; path of centers ; central trajectory
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper proposes an interior point algorithm for a positive semi-definite linear complementarity problem: find an (x, y)∈ℝ 2n such thaty=Mx+q, (x,y)⩾0 andx T y=0. The algorithm reduces the potential function $$f(x,y) = (n + \sqrt n )\log x^T y - \sum\limits_{i = 1}^n {\log x_i y_i } $$ by at least 0.2 in each iteration requiring O(n 3) arithmetic operations. If it starts from an interior feasible solution with the potential function value bounded by $$O(\sqrt n L)$$ , it generates, in at most $$O(\sqrt n L)$$ iterations, an approximate solution with the potential function value $$ - O(\sqrt n L)$$ , from which we can compute an exact solution in O(n 3) arithmetic operations. The algorithm is closely related with the central path following algorithm recently given by the authors. We also suggest a unified model for both potential reduction and path following algorithms for positive semi-definite linear complementarity problems.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 62 (1993), S. 85-93 
    ISSN: 1436-4646
    Keywords: Bigℳ ; affine scaling algorithm ; linear program ; interior point algorithm ; infeasibility ; global convergence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract When we apply the affine scaling algorithm to a linear program, we usually construct an artificial linear program having an interior feasible solution from which the algorithm starts. The artificial linear program involves a positive number called the bigℳ. Theoretically, there exists anℳ * such that the original problem to be solved is equivalent to the artificial linear program ifℳ 〉ℳ *. Practically, however, such anℳ * is unknown and a safe estimate ofℳ is often too large. This paper proposes a method of updatingℳ to a suitable value during the iteration of the affine scaling algorithm. Asℳ becomes large, the method gives information on infeasibility of the original problem or its dual.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 48 (1990), S. 415-435 
    ISSN: 1436-4646
    Keywords: Linear complementarity problem ; ellipsoid ; interior point algorithm ; path following algorithm
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper deals with the LCP (linear complementarity problem) with a positive semi-definite matrix. Assuming that a strictly positive feasible solution of the LCP is available, we propose ellipsoids each of which contains all the solutions of the LCP. We use such an ellipsoid for computing a lower bound and an upper bound for each coordinate of the solutions of the LCP. We can apply the lower bound to test whether a given variable is positive over the solution set of the LCP. That is, if the lower bound is positive, we know that the variable is positive over the solution set of the LCP; hence, by the complementarity condition, its complement is zero. In this case we can eliminate the variable and its complement from the LCP. We also show how we efficiently combine the ellipsoid method for computing bounds for the solution set with the path-following algorithm proposed by the authors for the LCP. If the LCP has a unique non-degenerate solution, the lower bound and the upper bound for the solution, computed at each iteration of the path-following algorithm, both converge to the solution of the LCP.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Annals of operations research 62 (1996), S. 1-28 
    ISSN: 1572-9338
    Keywords: Linear program ; interior point method ; initial point ; step length ; complementarity problem ; polynomial-time complexity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract The primal-dual infeasible-interior-point algorithm is known as one of the most efficient computational methods for linear programs. Recently, a polynomial-time computational complexity bound was established for special variants of the algorithm. However, they impose severe restrictions on initial points and require a common step length in the primal and dual spaces. This paper presents some basic lemmas that bring great flexibility and improvement into such restrictions on initial points and step lengths, and discusses their extensions to linear and nonlinear monotone complementarity problems.
    Type of Medium: Electronic Resource
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 65 (1994), S. 43-72 
    ISSN: 1436-4646
    Keywords: Monotone complementarity problem ; Interior-point algorithm ; Potential reduction algorithm ; Infeasibility ; Global convergence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper presents a wide class of globally convergent interior-point algorithms for the nonlinear complementarity problem with a continuously differentiable monotone mapping in terms of a unified global convergence theory given by Polak in 1971 for general nonlinear programs. The class of algorithms is characterized as: Move in a Newton direction for approximating a point on the path of centers of the complementarity problem at each iteration. Starting from a strictly positive but infeasible initial point, each algorithm in the class either generates an approximate solution with a given accuracy or provides us with information that the complementarity problem has no solution in a given bounded set. We present three typical examples of our interior-point algorithms, a horn neighborhood model, a constrained potential reduction model with the use of the standard potential function, and a pure potential reduction model with the use of a new potential function.
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 1 (1986), S. 499-515 
    ISSN: 1432-0541
    Keywords: Linear program ; Karmarkar's algorithm ; Optimal basis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper establishes a sufficient condition for a variable of a linear program to be positive at all optimal solutions. A numerical test using the condition is incorporated into Karmarkar's new LP algorithm to determine columns of optimal basis. Experimental results on the test are also reported.
    Type of Medium: Electronic Resource
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