We propose a novel method to compute globally injective parameterizations with arbitrary positional constraints on disk topology meshes.Central to this method is the use of a scaffold mesh that reduces the globally in...We propose a novel method to compute globally injective parameterizations with arbitrary positional constraints on disk topology meshes.Central to this method is the use of a scaffold mesh that reduces the globally injective constraint to a locally flipfree condition.Hence,given an initial parameterized mesh containing flipped triangles and satisfying the positional constraints,we only need to remove the flips of a overall mesh consisting of the parameterized mesh and the scaffold mesh while always meeting positional constraints.To successfully apply this idea,we develop two key techniques.Firstly,an initialization method is used to generate a valid scaffold mesh and mitigate difficulties in eliminating flips.Secondly,edgebased remeshing is used to optimize the regularity of the scaffold mesh containing flips,thereby improving practical robustness.Compared to state-of-the-art methods,our method is much more robust.We demonstrate the capability and feasibility of our method on a large number of complex meshes.展开更多
It iswell known that traditionalmean-variance optimal portfolio delivers rather erratic and unsatisfactory out-of-sample performance due to the neglect of estimation errors.Constrained solutions,such as no-short-sale-...It iswell known that traditionalmean-variance optimal portfolio delivers rather erratic and unsatisfactory out-of-sample performance due to the neglect of estimation errors.Constrained solutions,such as no-short-sale-constrained and norm-constrained portfolios,can usually achieve much higher ex post Sharpe ratio.Bayesian methods have also been shown to be superior to traditional plug-in estimator by incorporating parameter uncertainty through prior distributions.In this paper,we develop an innovative method that induces priors directly on optimal portfolio weights and imposing constraints a priori in our hierarchical Bayes model.We showthat such constructed portfolios are well diversified with superior out-of-sample performance.Our proposed model is tested on a number of Fama–French industry portfolios against the na飗e diversification strategy and Chevrier and McCulloch’s(2008)economically motivated prior(EMP)strategy.On average,our model outperforms Chevrier and McCulloch’s(2008)EMP strategy by over 15%and outperform the‘1/N’strategy by over 50%.展开更多
基金supported by the National Natural Science Foundation of China(61802359,62025207)USTC Research Funds of the Double First-Class Initiative(YD0010002003).
文摘We propose a novel method to compute globally injective parameterizations with arbitrary positional constraints on disk topology meshes.Central to this method is the use of a scaffold mesh that reduces the globally injective constraint to a locally flipfree condition.Hence,given an initial parameterized mesh containing flipped triangles and satisfying the positional constraints,we only need to remove the flips of a overall mesh consisting of the parameterized mesh and the scaffold mesh while always meeting positional constraints.To successfully apply this idea,we develop two key techniques.Firstly,an initialization method is used to generate a valid scaffold mesh and mitigate difficulties in eliminating flips.Secondly,edgebased remeshing is used to optimize the regularity of the scaffold mesh containing flips,thereby improving practical robustness.Compared to state-of-the-art methods,our method is much more robust.We demonstrate the capability and feasibility of our method on a large number of complex meshes.
基金This work was supported in part by US National Science Foundation(NSF)under grant DMS-1613110。
文摘It iswell known that traditionalmean-variance optimal portfolio delivers rather erratic and unsatisfactory out-of-sample performance due to the neglect of estimation errors.Constrained solutions,such as no-short-sale-constrained and norm-constrained portfolios,can usually achieve much higher ex post Sharpe ratio.Bayesian methods have also been shown to be superior to traditional plug-in estimator by incorporating parameter uncertainty through prior distributions.In this paper,we develop an innovative method that induces priors directly on optimal portfolio weights and imposing constraints a priori in our hierarchical Bayes model.We showthat such constructed portfolios are well diversified with superior out-of-sample performance.Our proposed model is tested on a number of Fama–French industry portfolios against the na飗e diversification strategy and Chevrier and McCulloch’s(2008)economically motivated prior(EMP)strategy.On average,our model outperforms Chevrier and McCulloch’s(2008)EMP strategy by over 15%and outperform the‘1/N’strategy by over 50%.
