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Shock compression of FeOOH and implications for iron-water interactions in super-earth magma oceans. [PDF]
Nat CommunZhang Y +21 more
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Mycelium-Based Composites for Interior Architecture: Digital Fabrication of Acoustic Ceiling Components. [PDF]
Biomimetics (Basel)Özkan M, Güzelci OZ.
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2017
One of the most powerful methods for solving nonlinear optimization problems known as interior point methods is to be presented in this chapter. They are related to barrier functions. The terms “interior point methods” and “barrier methods” have the same significance and may be used interchangeably.
Richard W. Cottle, Mukund N. Thapa
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One of the most powerful methods for solving nonlinear optimization problems known as interior point methods is to be presented in this chapter. They are related to barrier functions. The terms “interior point methods” and “barrier methods” have the same significance and may be used interchangeably.
Richard W. Cottle, Mukund N. Thapa
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Linear and Nonlinear Programming, 2008
Linear programs can be viewed in two somewhat complementary ways. They are, in one view, a class of continuous optimization problems each with continuous variables defined on a convex feasible region and with a continuous objective function. They are, therefore, a special case of the general form of problem considered in this text.
David G. Luenberger, Yinyu Ye
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Linear programs can be viewed in two somewhat complementary ways. They are, in one view, a class of continuous optimization problems each with continuous variables defined on a convex feasible region and with a continuous objective function. They are, therefore, a special case of the general form of problem considered in this text.
David G. Luenberger, Yinyu Ye
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Quantum speedups for linear programming via interior point methods
SIAM journal on computing (Print), 2023We describe a quantum algorithm based on an interior point method for solving a linear program with $n$ inequality constraints on $d$ variables. The algorithm explicitly returns a feasible solution that is $\varepsilon$-close to optimal, and runs in time
Simon Apers, S. Gribling
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Primal-Dual Interior-Point Methods
Other Titles in Applied Mathematics, 1997Preface Notation 1. Introduction. Linear Programming Primal-Dual Methods The Central Path A Primal-Dual Framework Path-Following Methods Potential-Reduction Methods Infeasible Starting Points Superlinear Convergence Extensions Mehrotra's Predictor ...
Stephen J. Wright
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Journal of Optimization Theory and Applications, 1998
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2013
The ellipsoid method has an undeniable historical relevance (due to its role in establishing polynomial time for linear programming with integer data). In addition, its underlying idea is simple and elegant. Unfortunately, it is not efficient in practice compared with both the simplex method and the more recent interior-point methods.
Peter Bürgisser, Felipe Cucker
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The ellipsoid method has an undeniable historical relevance (due to its role in establishing polynomial time for linear programming with integer data). In addition, its underlying idea is simple and elegant. Unfortunately, it is not efficient in practice compared with both the simplex method and the more recent interior-point methods.
Peter Bürgisser, Felipe Cucker
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1996
It was mentioned earlier that the standard simplex method searches for an optimum to a linear program by moving along the surface of a convex polyhedron from one extreme point to an adjacent extreme point in a fashion such that the objective value is nondecreasing between successive basic feasible solutions.
Cornelis Roos, Jean-Philippe Vial
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It was mentioned earlier that the standard simplex method searches for an optimum to a linear program by moving along the surface of a convex polyhedron from one extreme point to an adjacent extreme point in a fashion such that the objective value is nondecreasing between successive basic feasible solutions.
Cornelis Roos, Jean-Philippe Vial
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