A new polynomial-time algorithm for linear programming
Combinatorica
Recovering optimal dual solutions on Karmarkar's polynomial algorithm for linear programming
Mathematical Programming: Series A and B
A polynomial-time algorithm, based on Newton's method, for linear programming
Mathematical Programming: Series A and B
An algorithm for linear programming which requires O((m+n)n2 + (m+n)1.5n)L) arithmetic operations
Mathematical Programming: Series A and B
A random polynomial-time algorithm for approximating the volume of convex bodies
Journal of the ACM (JACM)
A new algorithm for minimizing convex functions over convex sets
Mathematical Programming: Series A and B
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Random walks and an O*(n5) volume algorithm for convex bodies
Random Structures & Algorithms
Self-scaled barriers and interior-point methods for convex programming
Mathematics of Operations Research
Improved bounds for sampling contingency tables
Random Structures & Algorithms
SIAM Journal on Computing
Fast Algorithms for Logconcave Functions: Sampling, Rounding, Integration and Optimization
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
The geometry of logconcave functions and sampling algorithms
Random Structures & Algorithms
Projective re-normalization for improving the behavior of a homogeneous conic linear system
Mathematical Programming: Series A and B
| Hi-index | 0.00 |
Let K be a polytope in Rn defined by m linear inequalities. We give a new Markov chain algorithm to draw a nearly uniform sample from K. The underlying Markov chain is the first to have a mixing time that is strongly polynomial when started from a “central” point. We use this result to design an affine interior point algorithm that does a single random walk to solve linear programs approximately.