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Recent results have shown that several H_2 and H_2-related problems can be formulated as convex programs with a finite number of variables. We present an interior point algorithm for the solution of t...
Robust Optimal Control of Linear Discrete-Time Systems Using Primal-Dual Interior-Point Methods
Robust Optimal Control Linear Discrete-Time Systems Primal-Dual Interior-Point Methods
2015/7/10
This paper describes how to efficiently solve a robust optimal control problem using recently developed primal-dual interior-point methods. Among potential applications are model predictive control. T...
Advances in Convex Optimization:Interior-point Methods,Cone Programming, and Applications
Advances in Convex Optimization Interior-point Methods Cone Programming Applications
2015/7/10
In this talk I will give an overview of some major developments in convex optimization that have emerged over the last ten years or so. The basic idea is that convex problems are fundamentally tractab...
Cutting-Set Methods for Robust Convex Optimization with Pessimizing Oracles
robust optimization cutting-set methods semi-infi nite programming minimax optimization
2015/7/9
We consider a general worst-case robust convex optimization problem, with arbitrary dependence on the uncertain parameters, which are assumed to lie in some given set of possible values. We describe a...
On the Marginal Standard Error Rule and the Testing of Initial Transient Deletion Methods
Marginal Standard Error Rule Testing Initial Transient Deletion Methods
2015/7/6
In this paper, we introduce several theoretically useful measures for the magni- tude of the initial transient in the setting of single replication steady-state simulations. These measures help suppor...
Laplace Inversion of Low-Resolution NMR Relaxometry Data Using Sparse Representation Methods
low-resolution NMR sparse reconstruction
2015/7/3
Low-resolution nuclear magnetic resonance (LR-NMR) relaxometry is a
powerful tool that can be harnessed for characterizing constituents in complex materials.
PROXIMAL NEWTON-TYPE METHODS FOR MINIMIZING COMPOSITE FUNCTIONS
convex optimization nonsmooth optimization
2015/7/3
We generalize Newton-type methods for minimizing smooth functions to handle a
sum of two convex functions: a smooth function and a nonsmooth function with a simple proximal mapping.
Cholesky-based Methods for Sparse Least Squares: The Beneˉts of Regularization
Sparse Least Squares Regularization
2015/7/3
We study the use of black-box LDL
T
factorizations for solving the augmented
systems (KKT systems) associated with least-squares problems and barrier methods
for linear programming (LP). With judi...
SOLVING REGULARIZED LINEAR PROGRAMS USING BARRIER METHODS AND KKT SYSTEMS
barrier methods interior methods linear programming
2015/7/3
We discuss the solution of regularized linear programs using a primal-dual barrier
method. Our implementation is based on indeˉnite Cholesky-type factorizations of full and reduced
KKT systems. Regu...
ON PROJECTED NEWTON BARRIER METHODS FOR LINEAR PROGRAMMING AND AN EQUIVALENCE TO KARMARKAR'S PROJECTIVE METHOD
Linear programming Karmarkar's method
2015/7/3
Interest in linear programming has been intensified recently by Karmarkar's publication in 1984
of an algorithm that is claimed to be much faster than the simplex method for practical problems.
We...
Optimization algorithms typically require the solution of many systems of linear equations
Bkyk b,. When large numbers of variables or constraints are present, these linear systems could account
for...
The Curse of Expertise: The Effects of Expertise and Debiasing Methods on Predictions of Novice Performance
Expertise Debiasing Methods
2015/7/3
Experts are often called on to predict the performance of novices, but
cognitive heuristics may interfere with experts' ability to capitalize on their
superior knowledge hi predicting novice task pe...