Works matching AU Anstreicher, Kurt M.
Results: 24
Solving Two-Trust-Region Subproblems Using Semidefinite Optimization with Eigenvector Branching.
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- Journal of Optimization Theory & Applications, 2024, v. 202, n. 1, p. 303, doi. 10.1007/s10957-022-02064-5
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- Article
Large step volumetric potential reduction algorithms for linear programming.
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- Annals of Operations Research, 1996, v. 62, n. 1-4, p. 521, doi. 10.1007/BF02206828
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Preface.
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- Annals of Operations Research, 1996, v. 62, n. 1-4, p. 1, doi. 10.1007/BF02206808
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Solving quadratic assignment problems using convex quadratic programming relaxations.
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- Optimization Methods & Software, 2001, v. 16, n. 1-4, p. 49, doi. 10.1080/10556780108805828
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A partial updating algorithm for linear programs with many more variables than constraints.
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- Optimization Methods & Software, 1994, v. 4, n. 4, p. 243, doi. 10.1080/10556789508805591
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On the convergence of an infeasible primal-dual interior-point method for convex programming.
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- Optimization Methods & Software, 1994, v. 3, n. 4, p. 273, doi. 10.1080/10556789408805570
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A monotonic build-up simplex algorithm for linear programming.
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- Operations Research, 1994, v. 42, n. 3, p. 556, doi. 10.1287/opre.42.3.556
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A family of search directions for Karmarkar's algorithm.
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- Operations Research, 1993, v. 41, n. 4, p. 759, doi. 10.1287/opre.41.4.759
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Convex hull representations for bounded products of variables.
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- Journal of Global Optimization, 2021, v. 80, n. 4, p. 757, doi. 10.1007/s10898-021-01046-7
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- Article
Maximum-entropy sampling and the Boolean quadric polytope.
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- Journal of Global Optimization, 2018, v. 72, n. 4, p. 603, doi. 10.1007/s10898-018-0662-x
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Semidefinite programming versus the reformulation-linearization technique for nonconvex quadratically constrained quadratic programming.
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- Journal of Global Optimization, 2009, v. 43, n. 2/3, p. 471, doi. 10.1007/s10898-008-9372-0
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Efficient Solution of Maximum-Entropy Sampling Problems.
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- Operations Research, 2020, v. 68, n. 6, p. 1826, doi. 10.1287/opre.2019.1962
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Quadratic optimization with switching variables: the convex hull for n=2.
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- Mathematical Programming, 2021, v. 188, n. 2, p. 421, doi. 10.1007/s10107-021-01671-w
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Computable representations for convex hulls of low-dimensional quadratic forms.
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- Mathematical Programming, 2010, v. 124, n. 1/2, p. 33, doi. 10.1007/s10107-010-0355-9
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Two “well-known” properties of subgradient optimization.
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- Mathematical Programming, 2009, v. 120, n. 1, p. 213, doi. 10.1007/s10107-007-0148-y
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Continuous optimization: 5th Brazilian workshop.
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- Mathematical Programming, 2008, v. 111, n. 1/2, p. 1, doi. 10.1007/s10107-006-0078-0
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- Article
The volumetric barrier for convex quadratic constraints.
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- Mathematical Programming, 2004, v. 100, n. 3, p. 613, doi. 10.1007/s10107-003-0513-4
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Recent advances in the solution of quadratic assignment problems.
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- Mathematical Programming, 2003, v. 97, n. 1/2, p. 27, doi. 10.1007/s10107-003-0437-z
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A new bound for the quadratic assignment problem based on convex quadratic programming.
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- Mathematical Programming, 2001, v. 89, n. 3, p. 341, doi. 10.1007/PL00011402
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Using continuous nonlinear relaxations to solve. constrained maximum-entropy sampling problems.
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- Mathematical Programming, 1999, v. 85, n. 2, p. 221, doi. 10.1007/s101070050055
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- Article
THE VOLUMETRIC BARRIER FOR SEMIDEFINITE PROGRAMMING.
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- Mathematics of Operations Research, 2000, v. 25, n. 3, p. 365, doi. 10.1287/moor.25.3.365.12212
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- Article
PROBABILISTIC ANALYSIS OF AN INFEASIBLE-INTERIOR-POINT ALGORITHM FOR LINEAR PROGRAMMING.
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- Mathematics of Operations Research, 1999, v. 24, n. 1, p. 176, doi. 10.1287/moor.24.1.176
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ON VAIDYA'S VOLUMETRIC CUTTING PLANE METHOD FOR CONVEX PROGRAMMING.
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- Mathematics of Operations Research, 1997, v. 22, n. 1, p. 63, doi. 10.1287/moor.22.1.63
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THE WORST-CASE STEP IN KARMARKAR'S ALGORITHM.
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- Mathematics of Operations Research, 1989, v. 14, n. 2, p. 294, doi. 10.1287/moor.14.2.294
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- Article