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- MATLAB code for solving elliptic diffusion-type problems, including Poisson’s equation on single patch ... apparent. It is not intended for large-scale problems...
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- methods and software for solving large-scale problems. Moreover, both fundamental building blocks, namely mixed...
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- software package for solving large-scale nonlinear optimization problems. This book provides a coherent overview ... proposal for a standard input for problems and the LANCELOT optimization package. Although the book ... design and implementation of large-scale optimization algorithms...
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- SVMTorch: Support vector machines for large-scale regression problems. Support Vector Machines (SVMs) for regression ... Light proposed by T. Joachims [“Making large-scale support vector machine learning practical ... efficiently solve large-scale regression problems (more than 20000 examples). Comparisons with Nodelib, another publicly ... available SVM algorithm for large-scale regression problems from G. Flake and S. Lawrence [Mach...
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- BFGS-B Fortran subroutines for large-scale bound-constrained optimization. L-BFGS ... limited-memory algorithm for solving large nonlinear optimization problems subject to simple bounds...
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- large-scale nonlinear optimization. It is designed to find (local) solutions of mathematical optimization problems...
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- SNOPT: An SQP algorithm for large-scale constrained optimization. Sequential quadratic programming (SQP) methods have ... proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective...
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- LIPSOL is designed to solve relatively large problems. It utilizes Matlab’s sparse-matrix data ... existing, efficient Fortran codes for solving large, sparse, symmetric positive deﬁnite linear systems. Specifically, LIPSOL ... adequate speed for solving moderately large-scale problems even in the presence of overhead induced...
- Referenced in 153 articles
- SPGL1: A solver for large-scale sparse reconstruction: Probing the Pareto frontier for basis pursuit ... solutions. The basis pursuit problem seeks a minimum one-norm solution of an underdetermined least...
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- method) in the solution of large-scale unconstrained minimization problems. It is shown...
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- since it makes MKL tractable for large-scale problems, by iteratively using existing support vector...
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- objects as accurately as possible. Such ranking problems naturally occur in applications like search engines ... large-scale transductive SVMs. The algorithm proceeds by solving a sequence of optimization problems lower...
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- powerful approach to designing solvers for large-scale problems in computational mechanics. The numerical simulation...
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- approximation can be excessively high for large-scale problems due to the need for solving...
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- ideally suited for solving large-scale compressed sensing reconstruction problems as (1) it is computationally...
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- programming systems. Mercury addresses the problems of large-scale program development, allowing modularity, separate compilation...
- Referenced in 1092 articles
- building blocks for the implementation of large-scale application codes on parallel (and serial) computers ... that is most appropriate for a particular problem. By using techniques of object-oriented programming...
- Referenced in 287 articles
- BFGS-B Fortran subroutines for large-scale bound-constrained optimization L-BFGS ... limited-memory algorithm for solving large nonlinear optimization problems subject to simple bounds...
- Referenced in 33 articles
- bundle method for large-scale nonsmooth optimization Many practical optimization problems involve nonsmooth (that ... hundreds or thousands of variables. In such problems the direct application of smooth gradient-based ... nonsmooth optimization methods is efficient in large-scale settings. In this article we describe ... academic test problems for large-scale nonsmooth minimization. Finally, we give some encouraging results from...
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- graph visualization; (4) a minor convergence problem of the graphical lasso algorithm is corrected ... lossy screening rules to scale up large-scale problems, making a tradeoff between computational...