Bernd Gärtner
- 1.P. K. Agarwal, B. Aronov, S. Har-Peled, and M. Sharir. Approximation and exact algorithms for minimum-width annuli and shells. In Proc. 15th Annu. A CM Sympos. Comput. Geom., pages 380-389, 1999. Google Scholar
Digital Library
- 2.V. Chv~tal. Linear Programming. W. H. Freeman, New York, NY, 1983.Google Scholar
- 3.A. Fabri, G.-J. Giezeman, L. Kettner, S. Schirra, and S. SchSnherr. On the design of CGAL, a computational geometry algorithms library. Software - Practice and Experience, 2000. to appear. Google ScholarDigital Library
- 4.B. G~tner. Exact arithmetic at low cost - a case study in linear programming. Computational Geometry- Theory and Applications, 13:121-139, 1999. Google ScholarDigital Library
- 5.B. G~tner. Fast and robust smallest enclosing balls. In Proc. 7th Annu. A CM Syrup. on Algorithms (ESA), volume 1643 of Lecture Notes in Computer Science, pages 325-338, 1999. Google ScholarDigital Library
- 6.B. G~tner. Pitfalls in computing with pseudorandom determinants. In Proc. 16th Annu. ACM Sympos. Comput. Geom., 2000. Google ScholarDigital Library
- 7.B. G~tner and E. Welzl. Random sampling in geometric optimization: new insights and applications. In Proc. 16th Annu. A CM Sympos. Comput. Geom., 2OOO. Google ScholarDigital Library
- 8.R. Kumar and D. Sivakumar. Roundness estimation via random sampling. In Proc. l Oth A CM-SIAM Sympos. Discrete Algorithms, pages 603-612, 1999. Google ScholarDigital Library
- 9.J. Matou~ek, M. Sharir, and E. Welzl. A subexponential bound for linear programming. Algorithmica, 16:498-516, 1996.Google ScholarCross Ref
- 10.K. Mehlhorn, S. N~iher, M. Seel, and C. Uhrig. The LEDA User Manual, 1999. Version 4.0.Google Scholar
- 11.D. Musser and A. Saini. STL Tutorial and Reference Guide: C++ Programming with the Standard Template Library. Addison-Wesley, 1996. Google ScholarDigital Library
- 12.A. L. Peressini, F. E. Sullivan, and J. J. Uhl. The Mathematics of Nonlinear Programming. Undergraduate Texts in Mathematics. Springer-Verlag, 1988. Google ScholarDigital Library
- 13.K. Sekitani and Y. Yamamoto. A recursive algorithm for finding the minimum norm point in a polytope an a pair of closest points in two polytopes. Math. Programming, 61(2):233-249, 1993. Google ScholarDigital Library
- 14.The CGAL Consortium. The CGAL Reference Manual, 2000. Version 2.1.Google Scholar
- 15.E. Welzl. Smallest enclosing disks (balls and ellipsoids). In H. Maurer, editor, New Results and Ne Trends in Computer Science, volume 555 of Lecture Notes Comput. Sci., pages 359-370. Springer-Verlag, 1991.Google Scholar
- 16.P. Wolfe. The simplex method for quadratic programming. Econometrica, 27:382-398, 1959.Google ScholarCross Ref
- 17.P. Wolfe. Finding the nearest point in a polytope. Math. Programming, 11:128-149, 1976.Google ScholarDigital Library
Index Terms
- An efficient, exact, and generic quadratic programming solver for geometric optimization
Recommendations
Using a Mixed Integer Quadratic Programming Solver for the Unconstrained Quadratic 0-1 Problem
In this paper, we consider problem (P) of minimizing a quadratic function q(x)=xtQx+ctx of binary variables. Our main idea is to use the recent Mixed Integer Quadratic Programming (MIQP) solvers. But, for this, we have to first convexify the objective ...
Mixed-integer quadratic programming
This paper considers mixed-integer quadratic programs in which the objective function is quadratic in the integer and in the continuous variables, and the constraints are linear in the variables of both types. The generalized Benders' decomposition is a ...
Exact quadratic convex reformulations of mixed-integer quadratically constrained problems
We propose a solution approach for the general problem (QP) of minimizing a quadratic function of bounded integer variables subject to a set of quadratic constraints. The resolution is based on the reformulation of the original problem (QP) into an ...
Comments