Enhanced inferencing: estimation of a workload dependent performance model
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
Interfaces
Structural and Multidisciplinary Optimization
Computational Optimization and Applications
Journal of Computational and Applied Mathematics
A scalable TFETI based algorithm for 2d and 3d frictionless contact problems
LSSC'09 Proceedings of the 7th international conference on Large-Scale Scientific Computing
Journal of Computational Physics
Handling infeasibility in a large-scale nonlinear optimization algorithm
Numerical Algorithms
SIAM Journal on Scientific Computing
Mathematics and Computers in Simulation
Mathematics and Computers in Simulation
BEM solution of delamination problems using an interface damage and plasticity model
Computational Mechanics
SuperQ: Computing Supernetworks from Quartets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Particle filter with multimode sampling strategy
Signal Processing
Regularized robust optimization: the optimal portfolio execution case
Computational Optimization and Applications
Physics-based animation of large-scale splashing liquids
ACM Transactions on Graphics (TOG)
Computers & Mathematics with Applications
A domain decomposition method for two-body contact problems with Tresca friction
Advances in Computational Mathematics
Journal of Global Optimization
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Solving optimization problems in complex systems often requires the implementation of advanced mathematical techniques. Quadratic programming (QP) is one technique that allows for the optimization of a quadratic function in several variables in the presence of linear constraints. QP problems arise in fields as diverse as electrical engineering, agricultural planning, and optics. Given its broad applicability, a comprehensive understanding of quadratic programming is a valuable resource in nearly every scientific field. Optimal Quadratic Programming Algorithms presents recently developed algorithms for solving large QP problems. The presentation focuses on algorithms which are, in a sense optimal, i.e., they can solve important classes of problems at a cost proportional to the number of unknowns. For each algorithm presented, the book details its classical predecessor, describes its drawbacks, introduces modifications that improve its performance, and demonstrates these improvements through numerical experiments. This self-contained monograph can serve as an introductory text on quadratic programming for graduate students and researchers. Additionally, since the solution of many nonlinear problems can be reduced to the solution of a sequence of QP problems, it can also be used as a convenient introduction to nonlinear programming. The reader is required to have a basic knowledge of calculus in several variables and linear algebra.