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Unifying Framework for Accelerated Randomized Methods in Convex Optimization
In this paper, we consider smooth convex optimization problems with simple constraints and inexactness in the oracle information such as value,...
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Sparse functional linear models via calibrated concave-convex procedure
In this paper, we propose a calibrated ConCave-Convex Procedure (CCCP) for variable selection in high-dimensional functional linear models. The...
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A New Algorithm for Convex Biclustering and Its Extension to the Compositional Data
Biclustering is a powerful data mining technique that allows simultaneously clustering rows (observations) and columns (features) in a matrix-format...
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Optimization
Optimization refers to the minimization (or maximization) of a given objective function of several decision variables that satisfy functional...
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Global Optimization for the Concave-Concave Multiplicative Programming with Coefficient
In this paper, we present a global optimization algorithm for globally solving the problem (CMPC) of minimizing a concave-concave multiplicative...
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A global two-stage algorithm for non-convex penalized high-dimensional linear regression problems
By the asymptotic oracle property, non-convex penalties represented by minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD)...
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Black-box optimization on hyper-rectangle using Recursive Modified Pattern Search and application to ROC-based Classification Problem
In statistics, it is common to encounter multi-modal and non-smooth likelihood (or objective function) maximization problems, where the parameters...
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Construction of optimal designs for quantile regression model via particle swarm optimization
As an extension of mean regression and being robust against outliers, quantile regression has been used in many fields such as biomedicine, ecology,...
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Bayesian sparse convex clustering via global-local shrinkage priors
Sparse convex clustering is to group observations and conduct variable selection simultaneously in the framework of convex clustering. Although a...
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Optimization of Redundancy Allocation Problem Using Quantum Particle Swarm Optimization Algorithm Under Uncertain Environment
Reliability optimization of a redundancy allocation problem is an important area of research in the literature. The main purpose of this type of...
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Optimization by Gradient Boosting
Gradient boosting is a state-of-the-art prediction technique that sequentially produces a model in the form of linear combinations of elementary...
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Reliability in Portfolio Optimization using Uncertain Estimates
Portfolio optimization problems are rather easy to solve if one assumes normality of the (joint) distribution of returns with given parameters and a...
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Estimating variances in time series kriging using convex optimization and empirical BLUPs
We revisit and update estimating variances, fundamental quantities in a time series forecasting approach called kriging, in time series models known...
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Portfolio Optimization With a Guaranteed Minimum Maturity Benefit and Risk-Adjusted Fees
We study a portfolio optimization problem involving the loss averse policyholder of a variable annuity with a guaranteed minimum maturity benefit....
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Extended mean-conditional value-at-risk portfolio optimization with PADM and conditional scenario reduction technique
In this paper, we study mean-conditional value-at-risk portfolio optimization problem with short selling, cardinality constraints and transaction...
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Multi-Objective Optimization
This chapter is dedicated to multi-objective optimization. First, three demonstrative multi-objective tasks are presented. Then, three main...
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Constrained Optimization
This chapter discusses the case of constrained optimization, and the additional steps needed to perform Bayesian Optimization in the presence of...
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Bivariate Block and Basu’s Exponential Distribution Through Entropy Optimization and Its Application to Rainfall Data
The q-bivariate Block and Basu’s exponential distribution (q-BBBED) is a generalized version of the bivariate Block and Basu’s exponential...
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Machine Learning, Regression and Optimization
Machine learning is a subfield of artificial intelligence (AI). While AI is the ability of the machine to think like humans, machine learning is the...
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Bayesian and maximin A-optimal designs for spline regression models with unknown knots
Optimal designs for spline regression models with multiple unknown knots are investigated using A-optimality, Bayesian and maximin criteria. Locally...
