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Escape from an Equilibrium: Importance Sampling and Rest Points
"... metastability Motivation: exit from an equilibrium and metastability ..."
Nonconvex factor adjustments in equilibrium business cycle models: do nonlinearities matter
 Journal of Monetary Economics
, 2003
"... Recent empirical analysis has found nonlinearities to be important in understanding aggregated investment. Using an equilibrium business cycle model, we search for aggregate nonlinearities arising from the introduction of nonconvex capital adjustment costs. We find that, while such costs lead to non ..."
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Cited by 45 (2 self)
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Recent empirical analysis has found nonlinearities to be important in understanding aggregated investment. Using an equilibrium business cycle model, we search for aggregate nonlinearities arising from the introduction of nonconvex capital adjustment costs. We find that, while such costs lead
Cascadic multilevel algorithms for symmetric saddle point systems
 Comput. Math. Appl
"... Abstract. In this paper, we introduce a multilevel algorithm for approximating variational formulations of symmetric saddle point systems. The algorithm is based on availability of families of stable finite element pairs and on the availability of fast and accurate solvers for symmetric positive ..."
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Cited by 2 (2 self)
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Abstract. In this paper, we introduce a multilevel algorithm for approximating variational formulations of symmetric saddle point systems. The algorithm is based on availability of families of stable finite element pairs and on the availability of fast and accurate solvers for symmetric positive
safetyseries No50SGD9 IAEA SAFETY GUIDES Design Aspects of Radiation Protection for Nuclear Power Plants
"... From Safety Series No. 46 onwards the various publications in the series are divided into four categories, as follows: ..."
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From Safety Series No. 46 onwards the various publications in the series are divided into four categories, as follows:
Fast optimization of nonconvex Machine Learning objectives
"... In this project we examined the problem of nonconvex optimization in the context of Machine Learning, drawing inspiration from the increasing popularity of methods such as Deep Belief Networks, which involve nonconvex objectives. We focused on the task of training the Neural Autoregressive Distrib ..."
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In this project we examined the problem of nonconvex optimization in the context of Machine Learning, drawing inspiration from the increasing popularity of methods such as Deep Belief Networks, which involve nonconvex objectives. We focused on the task of training the Neural Autoregressive
1 0 ON THE AFFINE HEAT EQUATIONFOR NONCONVEX CURVES
"... Abstract In this paper, we extend to the nonconvex case the affine invariant geometricheat equation studied in [30] for convex plane curves. We prove that a smooth embedded plane curve will converge to a point when evolving according to thisflow. This result extends the analogy between the affine h ..."
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Abstract In this paper, we extend to the nonconvex case the affine invariant geometricheat equation studied in [30] for convex plane curves. We prove that a smooth embedded plane curve will converge to a point when evolving according to thisflow. This result extends the analogy between the affine
Regularized Bundle Methods for Convex and NonConvex Risks
"... Machine learning is most often cast as an optimization problem. Ideally, one expects a convex objective function to rely on efficient convex optimizers with nice guarantees such as no local optima. Yet, nonconvexity is very frequent in practice and it may sometimes be inappropriate to look for conv ..."
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Cited by 3 (0 self)
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Machine learning is most often cast as an optimization problem. Ideally, one expects a convex objective function to rely on efficient convex optimizers with nice guarantees such as no local optima. Yet, nonconvexity is very frequent in practice and it may sometimes be inappropriate to look
1 0 ON THE AFFINE HEAT EQUATION FOR NONCONVEX CURVES
"... In this paper, we extend to the nonconvex case the affine invariant geometric heat equation studied in [30] for convex plane curves. We prove that a smooth embedded plane curve will converge to a point when evolving according to this flow. This result extends the analogy between the affine heat equ ..."
Abstract
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In this paper, we extend to the nonconvex case the affine invariant geometric heat equation studied in [30] for convex plane curves. We prove that a smooth embedded plane curve will converge to a point when evolving according to this flow. This result extends the analogy between the affine heat
Compressed sensing: How sharp is the RIP?
 SIAM REVIEW
"... Consider a measurement matrix A of size n×N, with n < N, y a signal in RN, and b = Ay the observed measurement of the vector y. From knowledge of (b, A), compressed sensing seeks to recover the ksparse x, k < n, which minimizes ‖b − Ax‖. Using various methods of analysis — convex polytopes, ..."
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Cited by 7 (2 self)
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Consider a measurement matrix A of size n×N, with n < N, y a signal in RN, and b = Ay the observed measurement of the vector y. From knowledge of (b, A), compressed sensing seeks to recover the ksparse x, k < n, which minimizes ‖b − Ax‖. Using various methods of analysis — convex polytopes
Results 1  10
of
9,899