Do you want to publish a course? Click here

Generalized Lasry – Lions regularization function using Bregman distant

تعميم دالة تنظيم Lasry – Lions باستخدام مسافة بريغمان

1285   1   37   0 ( 0 )
 Publication date 2014
  fields Mathematics
and research's language is العربية
 Created by Shamra Editor




Ask ChatGPT about the research

The purpose of the research is to study Bergman distance to generalize Lasry – Lions regularization which play important role of theory optimization. To do that we replace the quardatic additive terms in Lasry – Lions regularization by more general Bergman distance (non metric distance), and study properties generalized approximation and proof its continuous as we give a relationship between the solution minimization sets of function and Lions – Lasry Regularization and others properties.

References used
ATTOUCH, H.: Variational convergence for functions and operators , Pitman,London ( 1984), 120-264
ATTOUCH, H.; AZE, D.: Approximation and regularization of arbitrary functions in Hilbert space by the Lasry-Lions method, Ann. Inst. Henri Poincare 10(3) (1993), pp. 289–312
BAUSCHKE, H. H. ; BORWEIN,J.M. : Legendre functions and the method of random Bregman projections .Journal of Convex Analysis 4 (1997) 27-67
BAUSCHKE, H. H. ; BORWEIN, J. M. ; COMBETTES, P. L. :Bregman monotone optimization Algorithms, SIAM J. Control Optim, vol. 42, pp. 596–636, 2003
BAUSCHKE, H. H., COMBETTES, P. L , NOLL, D.: Joint minimization with alternating Bregman proximity operators, Pacific J. Optim. 2 (2006), 401–424
rate research

Read More

In this paper, spline approximations with five collocation points are used for the numerical simulation of stochastic of differential equations(SDE). First, we have modeled continuous-valued discrete wiener process, and then numerical asymptotic st ochastic stability of spline method is studied when applied to SDEs. The study shows that the method when applied to linear and nonlinear SDEs are stable and convergent. Moreover, the scheme is tested on two linear and nonlinear problems to illustrate the applicability and efficiency of the purposed method. Comparisons of our results with Euler–Maruyama method, Milstein’s method and Runge-Kutta method, it reveals that the our scheme is better than others.
It is often useful to replace a function with a sequence of smooth functions approximating the given function to resolve minimizing optimization problems. The most famous one is the Moreau envelope. Recently the function was organized using the Br egman distance h D . It is worth noting that Bregman distance h D is not a distance in the usual sense of the term. In general, it is not symmetric and it does not satisfy the triangle inequality The purpose of the research is to study the convergence of the Moreau envelope function and the related proximal mapping depends on Bregman Distance for a function on Banach space. Proved equivalence between Mosco-epi-convergence of sequence functions and pointwise convergence of Moreau-Bregman envelope We also studied the strong and weak convergence of resolvent operators According to the concept of Bregman distance.
Multilingual pretrained representations generally rely on subword segmentation algorithms to create a shared multilingual vocabulary. However, standard heuristic algorithms often lead to sub-optimal segmentation, especially for languages with limited amounts of data. In this paper, we take two major steps towards alleviating this problem. First, we demonstrate empirically that applying existing subword regularization methods (Kudo, 2018; Provilkov et al., 2020) during fine-tuning of pre-trained multilingual representations improves the effectiveness of cross-lingual transfer. Second, to take full advantage of different possible input segmentations, we propose Multi-view Subword Regularization (MVR), a method that enforces the consistency of predictors between using inputs tokenized by the standard and probabilistic segmentations. Results on the XTREME multilingual benchmark (Hu et al., 2020) show that MVR brings consistent improvements of up to 2.5 points over using standard segmentation algorithms.
In this paper, we study the Gamma function, and the representation of a complex variable, using either sequential or appropriate integration ,and its application in solving certain types of integral equations , and their relationship to the Riemann- Zeta function, and used to solve the contour integration ,and to find the integration of Hankel’s contour integral according to Bessel function for integer n.
Fine-tuning pre-trained language models suchas BERT has become a common practice dom-inating leaderboards across various NLP tasks.Despite its recent success and wide adoption,this process is unstable when there are onlya small number of training sam ples available.The brittleness of this process is often reflectedby the sensitivity to random seeds. In this pa-per, we propose to tackle this problem basedon the noise stability property of deep nets,which is investigated in recent literature (Aroraet al., 2018; Sanyal et al., 2020). Specifically,we introduce a novel and effective regulariza-tion method to improve fine-tuning on NLPtasks, referred to asLayer-wiseNoiseStabilityRegularization (LNSR). We extend the theo-ries about adding noise to the input and provethat our method gives a stabler regularizationeffect. We provide supportive evidence by ex-perimentally confirming that well-performingmodels show a low sensitivity to noise andfine-tuning with LNSR exhibits clearly bet-ter generalizability and stability. Furthermore,our method also demonstrates advantages overother state-of-the-art algorithms including L2-SP (Li et al., 2018), Mixout (Lee et al., 2020)and SMART (Jiang et al., 20)

suggested questions

comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا