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Jordan and G. Grant and S. Boyd, CVX: Matlab software for disciplined convex programming,, version 1. Khot and A. Naor, Grothendieck-type inequalities in combinatorial optimization,, Communications on Pure and Applied Mathematics , 65 , Kindler, A. Naor and G. Lovasz and A. Optimization , 1 , Luss and M. Pinar and M. Teboulle, On semidefinite bounds for maximization of a non-convex quadratic objective over the l1 unit ball,, RAIRO-Operations Research , 40 , Sturm, Using SeDuMi 1.
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Strongly convex programming for exact matrix completion and robust principal component analysis. A short note on strongly convex programming for exact matrix completion and robust principal component analysis. An integrated Principal Component Analysis and multi-objective mathematical programming approach to agile supply chain network design under uncertainty. Shouhong Yang. Semidefinite programming via image space analysis. Duality formulations in semidefinite programming.
Paul B. Hermanns , Nguyen Van Thoai. Global optimization algorithm for solving bilevel programming problems with quadratic lower levels. Songqiang Qiu , Zhongwen Chen. An adaptively regularized sequential quadratic programming method for equality constrained optimization. Shu-Cherng Fang , David Y. Canonical dual approach to solving quadratic programming problems. Volume 4 Issue 1 February Volume 3 Issue 4 November Volume 3 Issue 3 August Volume 3 Issue 2 May Volume 3 Issue 1 February Volume 2 Issue 4 November Volume 2 Issue 3 August Volume 2 Issue 2 May Volume 2 Issue 1 February Volume 1 Issue 4 November Volume 1 Issue 3 August Volume 1 Issue 2 May Volume 1 Issue 1 February View PDF.
Go to Section. Home Mathematics of Operations Research Vol. Previous Back to Top. Minh N. Dao , Hung M. Fixed points of polarity type operators. Optimal geospatial volunteer allocation needs realistic distances. Constraints reordering to speed up successive over-relaxation for constraint-based user interface layouts. Nonlinearity and solution techniques in reservoir simulation: A review. Measuring centrality and dispersion in directional datasets: the ellipsoidal cone covering approach.
Extending linear relaxation for non-square matrices and soft constraints. Central axes and peripheral points in high dimensional directional datasets.
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Towards a deeper geometric, analytic and algorithmic understanding of margins. A deterministic rescaled perceptron algorithm. Conic version of Loewner—John ellipsoid theorem. Convergence studies on block iterative algorithms for image reconstruction. A polynomial projection algorithm for linear feasibility problems. From cutting planes algorithms to compression schemes and active learning. A condition-based algorithm for solving polyhedral feasibility problems. On highly eccentric cones.https://es.iweqojylyz.cf
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Some preconditioners for systems of linear inequalities. On Chubanov's Method for Linear Programming. De Loera , Mark Junod. Robust smoothed analysis of a condition number for linear programming. A Smooth Perceptron Algorithm. Projection Methods. Algorithmic Operators. Convergence of Iterative Methods. Algorithmic Projection Operators. Zhang dynamics solving scalar-valued time-varying linear inequalities using different activation functions. Random projection algorithms for convex set intersection problems. On strata of degenerate polyhedral cones I: Condition and distance to strata.
A simple polynomial-time rescaling algorithm for solving linear programs. Normality and modulability indices. Part I: Convex cones in normed spaces. Nonnegative Moore—Penrose inverses of Gram operators. Boundedness Theorems for the Relaxation Method. Edoardo Amaldi , Raphael Hauser ,.
Robert M. Obtuse cones and Gram matrices with nonnegative inverse. Convergence Rate of Incremental Subgradient Algorithms. Monotone gram matrices and deepest surrogate inequalities in accelerated relaxation methods for convex feasibility problems. Error estimates and Lipschitz constants for best approximation in continuous function spaces. Primal-dual row-action method for convex programming. Geometrically convergent projection method in matrix games. Approximations to Solutions to Systems of Linear Inequalities.
Block-iterative surrogate projection methods for convex feasibility problems. About geometrical convergence of general iterative methods applied to nonunique solvable convex problems, Part I. About geometrical convergence of general iterative methods applied to nonunique solvable convex problems, Part II. Exposing Constraints.
Projection onto an acute cone and convex feasibility problem. The sharp Lipschitz constants for feasible and optimal solutions of a perturbed linear program. The foundations of set theoretic estimation. Surrogate methods for linear inequalities. A conjugate gradient algorithm for sparse linear inequalities. Relaxed outer projections, weighted averages and convex feasibility.
A class of methods for solving large convex systems. On the convergence properties of Hildreth's quadratic programming algorithm. On the behavior of a block-iterative projection method for solving convex feasibility problems. Cellular neural networks: Dynamic properties and adaptive learning algorithm. On some optimization techniques in image reconstruction from projections. A projection method for least-squares solutions to overdetermined systems of linear inequalities.
A primal-dual projection method for solving systems of linear inequalities. Variable metric relaxation methods, part II: The ellipsoid method. Convergence of the cyclical relaxation method for linear inequalities. Partial inverse of a monotone operator. On the non-polynomiality of the relaxation method for systems of linear inequalities.
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Quebec , the N. Neural network design using linear programming and relaxation. Volume 5, Issue 3 August Close Figure Viewer. Previous Figure Next Figure. Maximum Entropy and Constrained Optimization. Maximum Entropy and Bayesian Methods, Necessary Optimality Conditions via Image Problem.
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Nonsmooth Optimization and Related Topics, Civil Engineering Systems 5 :3, Engineering Optimization 12 :3, European Journal of Operational Research 31 :1, Non-linear separation theorems, duality and optimality conditions. Optimization and Related Fields, Optimization 16 :6, Journal of Optimization Theory and Applications 42 :3, Tind and L. Mathematical Programming 25 :2, Selected Bibliography of Works Not Cited. Mathematical Programming 21 :1, A I I E Transactions 13 :1, Journal of Optimization Theory and Applications 25 :2, Journal of Optimization Theory and Applications 19 :4, A four-variable world system.
Some remarks on generalized lagrangians. Journal of Optimization Theory and Applications 17 , Journal of Optimization Theory and Applications 15 :3,