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Showing 1 to 11 of 11 for “"second-order cone programming"”.
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Computation of upper and lower bounds in limit analysis using second-order cone programming and mesh adaptivity
… of an upper or a lower bound is guaranteed. The second step consists of solving the resulting discrete nonlinear optimization problems. Towards this end, they are reformulated into the canonical form of Second-order Cone Programs, which allows for the use of primal-dual interior point methods …
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Application of lower bound limit analysis with second-order cone programming for plane strain and axisymmetric geomechanics problems
… bound limit analysis with finite elements and second-order cone programming for various geotechnical stability analyses. For the plane strain case, we firstly investigate the bearing capacity of shallow foundations on sand subjected to combined loading. The results are represented as load …
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NOVEL NUMERICAL PROCEDURES FOR LIMIT ANALYSIS OF STRUCTURES: MESH-FREE METHODS AND MATHEMATICAL PROGRAMMING
… strategy adopted as well as on the mathematical programming tools applied, which are the key ingredients of a typical computational limit analysis procedure. In this research, the Element-Free Galerkin (EFG) discretisation strategy is used to approximate the displacement and moment fields in …
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Distributionally robust binary classifier under Wasserstein distance
… general problem boils down to an easy-to-solve second- order cone programming problem. The robustified SVM is then applied to synthetic data with and without contamination, and our simulation studies show that our robustified SVM model can outperform the classical SVM and the extreme empirical …
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Spatio-temporal analysis in functional brain imaging
… source estimates efficiently by solving a second-order cone programming problem. By considering all time points simultaneously, we achieve accurate and stable results as confirmed by the experiments using simulated and human MEG data. Although the l₁l₂-norm estimator enables accurate source …
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Robust optimization
… (QCQP), general conic optimization including second order cone programming (SOCP) and semidefinite optimization (SDP), mixed integer optimization (MIP), network flows and 0 - 1 discrete optimization. Our approach allows the modeler to vary the level of conservatism of the robust solutions in …
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Improvements in magnetic resonance imaging excitation pulse design
… which to approximately solve it. We find that second-order-cone programming and iteratively-reweighted least squares approaches are practical techniques for solving the relaxed problem and prove that single-vector sparse approximation of a complex-valued vector is an MSSO problem.
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Variable screening and graphical modeling for ultra-high dimensional longitudinal data
… used pairwise coordinate descent combined with second order cone programming to optimize the penalized likelihood and estimate the parameters. Furthermore, we extended the nodewise regression method the for longitudinal data case. Simulation and real data analysis exhibit the competitive …
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Mixed-integer convex optimization : outer approximation algorithms and modeling power
… convex optimization, or mixed-integer convex programming (MICP), the class of optimization problems where one seeks to minimize a convex objective function subject to convex constraints and integrality restrictions on a subset of the variables. We focus on two broad and complementary questions …
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Algorithms for Sparse and Low-Rank Optimization: Convergence, Complexity and Applications
… problems can be reformulated as either linear programming, second-order cone programming or semidefinite programming problems, the standard methods for solving these relaxations are not applicable because the problems are usually of huge size and contain dense data. In this dissertation, we …
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Dynamic decision-making under uncertainties: algorithms based on linear decision rules and applications in operating models
… nonlinear terms in the objective function. The second treats quadratic terms in the objective function by a Second-Order Cone approximation. The details and implementation of the proposed methods are presented in Chapter 3 and Chapter 4. Chapter 3 utilizes the Robust Optimization approach to …