#optimizationtheory — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #optimizationtheory, aggregated by home.social.
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`in this work we demonstrate, both theoretically and empirically, how to regularize a variational deep network implicitly via the optimization procedure, just as for standard deep learning. We fully characterize the inductive bias of (stochastic) gradient descent in the case of an overparametrized linear model as generalized variational inference and demonstrate the importance of the choice of parametrization.`
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`in this work we demonstrate, both theoretically and empirically, how to regularize a variational deep network implicitly via the optimization procedure, just as for standard deep learning. We fully characterize the inductive bias of (stochastic) gradient descent in the case of an overparametrized linear model as generalized variational inference and demonstrate the importance of the choice of parametrization.`
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`in this work we demonstrate, both theoretically and empirically, how to regularize a variational deep network implicitly via the optimization procedure, just as for standard deep learning. We fully characterize the inductive bias of (stochastic) gradient descent in the case of an overparametrized linear model as generalized variational inference and demonstrate the importance of the choice of parametrization.`
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`in this work we demonstrate, both theoretically and empirically, how to regularize a variational deep network implicitly via the optimization procedure, just as for standard deep learning. We fully characterize the inductive bias of (stochastic) gradient descent in the case of an overparametrized linear model as generalized variational inference and demonstrate the importance of the choice of parametrization.`
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`in this work we demonstrate, both theoretically and empirically, how to regularize a variational deep network implicitly via the optimization procedure, just as for standard deep learning. We fully characterize the inductive bias of (stochastic) gradient descent in the case of an overparametrized linear model as generalized variational inference and demonstrate the importance of the choice of parametrization.`
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`We show that the Weierstrass method, like the well known #Newton method, is not generally convergent: there are open sets of #polynomials p of every degree d≥3 such that the dynamics of the Weierstrass method applied to p exhibits attracting periodic orbits.`
https://arxiv.org/abs/2004.04777
#rootFinding #optimizationTheory #optimization #computation #computing
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`We show that the Weierstrass method, like the well known #Newton method, is not generally convergent: there are open sets of #polynomials p of every degree d≥3 such that the dynamics of the Weierstrass method applied to p exhibits attracting periodic orbits.`
https://arxiv.org/abs/2004.04777
#rootFinding #optimizationTheory #optimization #computation #computing
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`We show that the Weierstrass method, like the well known #Newton method, is not generally convergent: there are open sets of #polynomials p of every degree d≥3 such that the dynamics of the Weierstrass method applied to p exhibits attracting periodic orbits.`
https://arxiv.org/abs/2004.04777
#rootFinding #optimizationTheory #optimization #computation #computing
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`We show that the Weierstrass method, like the well known #Newton method, is not generally convergent: there are open sets of #polynomials p of every degree d≥3 such that the dynamics of the Weierstrass method applied to p exhibits attracting periodic orbits.`
https://arxiv.org/abs/2004.04777
#rootFinding #optimizationTheory #optimization #computation #computing
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`As in #biological #bee colonies, a small number of scouts keeps exploring the solution space looking for new regions of high fitness (global search). The global #search procedure re-initialises the last ns-nb #flower patches with randomly generated solutions.`
https://en.wikipedia.org/wiki/Bees_algorithm
#optimization #optimizationTheory #globalOptimization #algorithm #algorithms #searchAlgorithm #algorithmicSearch
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`As in #biological #bee colonies, a small number of scouts keeps exploring the solution space looking for new regions of high fitness (global search). The global #search procedure re-initialises the last ns-nb #flower patches with randomly generated solutions.`
https://en.wikipedia.org/wiki/Bees_algorithm
#optimization #optimizationTheory #globalOptimization #algorithm #algorithms #searchAlgorithm #algorithmicSearch
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`As in #biological #bee colonies, a small number of scouts keeps exploring the solution space looking for new regions of high fitness (global search). The global #search procedure re-initialises the last ns-nb #flower patches with randomly generated solutions.`
https://en.wikipedia.org/wiki/Bees_algorithm
#optimization #optimizationTheory #globalOptimization #algorithm #algorithms #searchAlgorithm #algorithmicSearch
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`As in #biological #bee colonies, a small number of scouts keeps exploring the solution space looking for new regions of high fitness (global search). The global #search procedure re-initialises the last ns-nb #flower patches with randomly generated solutions.`
https://en.wikipedia.org/wiki/Bees_algorithm
#optimization #optimizationTheory #globalOptimization #algorithm #algorithms #searchAlgorithm #algorithmicSearch
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`According to George Dantzig, the duality theorem for linear optimization was conjectured by John von Neumann immediately after Dantzig presented the linear programming problem. Von Neumann noted that he was using information from his game theory, and conjectured that two person zero sum matrix game was equivalent to linear programming. Rigorous proofs were first published in 1948 by Albert W. Tucker and his group`
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`According to George Dantzig, the duality theorem for linear optimization was conjectured by John von Neumann immediately after Dantzig presented the linear programming problem. Von Neumann noted that he was using information from his game theory, and conjectured that two person zero sum matrix game was equivalent to linear programming. Rigorous proofs were first published in 1948 by Albert W. Tucker and his group`
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`According to George Dantzig, the duality theorem for linear optimization was conjectured by John von Neumann immediately after Dantzig presented the linear programming problem. Von Neumann noted that he was using information from his game theory, and conjectured that two person zero sum matrix game was equivalent to linear programming. Rigorous proofs were first published in 1948 by Albert W. Tucker and his group`
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`According to George Dantzig, the duality theorem for linear optimization was conjectured by John von Neumann immediately after Dantzig presented the linear programming problem. Von Neumann noted that he was using information from his game theory, and conjectured that two person zero sum matrix game was equivalent to linear programming. Rigorous proofs were first published in 1948 by Albert W. Tucker and his group`