Category: optimizationPage 2 of 3
One of my academic goal is to establish a Bayesian risk management workflow in operations research, especially for the defense industry where I have great project opportunities through…
The current model in here is for prediction ie. the result is the optimal parameter value that minimizes the distance between prediction and real data. My goal is…
I am trying to making stan’s advi engine more robust in terms of the following: stopping rule values returned from each iteration 1 is decided based on 2…
My question was: nestrov accelerated gradient descent is represented as having iteration complexity of o() and convergence rate of . Are these the same? A) YES, depends on…
keyword: robust optimization, support division, multimodal distribution contribution: – suggests support division (SD) approach which addresses drawbacks of stochastic problem by transforming random variable from value to its…
I will try to update this blog which lists my understandings in each of the following areas in convex optimization (in order of my interest). Lagrange multipliers Lagrange…
SDP is a sort of cone-LP and optimize linear function over the intersection of an affine space and cone. It helps solving NP-hard combinatorial optimization including approximation algorithm…
I wish to solve multimodal distribution optimization in two stage approach. Example usecase is newsvendor problem; order quantity should be optimized for the multimodal-shaped demand due to its…
no theoretical guarantee for accurate results (Wang18) marginal variances of the parameters are often underestimated (Turner11) Turner11 introduce two problems in applying vEM to time series compactness– separated…
Applying the advantage of Bayesian paradigm(hierarchical modeling, coherent treatment of uncertainty) to big data setting is active in variational Bayes (VB). In VB, marginal likelihood’s variational lower bound…