Branch-and-bound approaches to standard quadratic optimization problems
Journal of Global Optimization
Solving Standard Quadratic Optimization Problems via Linear, Semidefinite and Copositive Programming
Journal of Global Optimization
Computational Optimization and Applications
Approximate minimum enclosing balls in high dimensions using core-sets
Journal of Experimental Algorithmics (JEA)
A New Self-Dual Embedding Method for Convex Programming
Journal of Global Optimization
Proceedings of the 2005 ACM symposium on Document engineering
Solving the simple continuous table layout problem
Proceedings of the 2006 ACM symposium on Document engineering
An Efficient Interior-Point Method for Convex Multicriteria Optimization Problems
Mathematics of Operations Research
A primal--dual symmetric relaxation for homogeneous conic systems
Journal of Complexity
Computational Optimization and Applications
Semidefinite programming and arithmetic circuit evaluation
Discrete Applied Mathematics
Parabolic target space and primal-dual interior-point methods
Discrete Applied Mathematics
Optimization Methods & Software - Dedicated to Professor Michael J.D. Powell on the occasion of his 70th birthday
Computational Optimization and Applications
Risk management in uncapacitated facility location models with random demands
Computers and Operations Research
Discretization method for semi-definite programming
Computers & Mathematics with Applications
Efficient linear precoding in downlink cooperative cellular networks with soft interference nulling
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Computers & Mathematics with Applications
Nonlinear network optimization: an embedding vector space approach
IEEE Transactions on Evolutionary Computation
Linear precoding in cooperative MIMO cellular networks with limited coordination clusters
IEEE Journal on Selected Areas in Communications - Special issue on cooperative communications in MIMO cellular networks
Near linear-work parallel SDD solvers, low-diameter decomposition, and low-stretch subgraphs
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
SIAM Journal on Optimization
Computation of channel capacity based on self-concordant functions
Journal of Electrical and Computer Engineering
Stochastic 0-1 linear programming under limited distributional information
Operations Research Letters
Polytopes and arrangements: Diameter and curvature
Operations Research Letters
Multivariate arrival rate estimation using semidefinite programming
Proceedings of the Winter Simulation Conference
An empirical evaluation of walk-and-round heuristics for mixed integer linear programs
Computational Optimization and Applications
An entire space polynomial-time algorithm for linear programming
Journal of Global Optimization
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This compact book, through the simplifying perspective it presents, will take a reader who knows little of interior-point methods to within sight of the research frontier, developing key ideas that were over a decade in the making by numerous interior-point method researchers. It aims at developing a thorough understanding of the most general theory for interior-point methods, a class of algorithms for convex optimization problems. The study of these algorithms has dominated the continuous optimization literature for nearly 15 years. In that time, the theory has matured tremendously, but much of the literature is difficult to understand, even for specialists. By focusing only on essential elements of the theory and emphasizing the underlying geometry, A Mathematical View of Interior-Point Methods in Convex Optimization makes the theory accessible to a wide audience, allowing them to quickly develop a fundamental understanding of the material.