Category: optimizationPage 2 of 3

My academic interests in 2021

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…

Decision analysis using Stan

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…

Stochastic optimization in variational inference

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…

iteration complexity and convergence rate

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…

Robust scenario approach for optimizing uncertainty

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…

Convex optimization

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_on_duality_complexity_application

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…

Time series, multimodal, mixture model, variational methods

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…

mean field variational bayes adversarial for improvement

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…

Broderick13_Streaming Variational Bayes

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…