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A Primal-Dual Operator Splitting Method for Conic Optimization
Primal-Dual Operator Splitting Method Conic Optimization
2015/7/9
We develop a simple operator splitting method for solving a primal conic optimization problem; we show that the iterates also solve the dual problem. The resulting algorithm is very simple to describe...
Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding
Optimization via Operator Splitting Homogeneous Self-Dual Embedding
2015/7/9
We introduce a first order method for solving very large cone programs to modest accuracy. The method uses an operator splitting method, the alternating directions method of multipliers, to solve the ...
Minimum-Time Speed Optimization Along a Fixed Path
minimum-time trajectory generation optimal speed control convex optimisation
2015/7/9
In this paper we investigate the problem of optimizing the speed of a vehicle over a fixed path for minimum time traversal. We utilize a change of variables that has been known since the 1980s, althou...
Block Splitting for Distributed Optimization
Distributed optimization · Alternating direction method of multipliers Operator splitting Proximal operators Cone programming Machine learning
2015/7/9
This paper describes a general purpose method for solving convex optimization problems in a distributed computing environment. In particular, if the problem data includes a large linear operator or ma...
Convex Optimization in Julia
Convex programming automatic verifcation symbolic computation multiple dispatch
2015/7/9
This paper describes Convex.jl, a convex optimization modeling framework in Julia. Convex.jl translates problems from a user-friendly functional language into an abstract syntax tree describing the pr...
We introduce a convex optimization modeling framework that transforms a convex optimization problem expressed in a form natural and convenient for the user into an equivalent cone program in a way tha...
Network Lasso: Clustering and Optimization in Large Graphs
Convex Optimization ADMM Network Lasso
2015/7/8
Convex optimization is an essential tool for modern data analysis, as it provides a framework to formulate and solve many problems in machine learning and data mining. However, general convex optimiza...
Complexity of Non-Adaptive Optimization Algorithms for a Class of Diffusions
Global optimization average-case complexity diffusion processes
2015/7/8
This paper is concerned with the analysis of the average error in approximating the global minimum of a 1-dimensional, time-homogeneous diffusion by non-adaptive methods. We derive the limiting distri...
This paper is concerned with the use of grid search as a means of optimizaing an objective function that can be evaluated only through simulation. We study the question of how rapidly the number of re...
A Large Deviations Perspective on Ordinal Optimization
Large Deviations Perspective Ordinal Optimization
2015/7/6
We consider the problem of optimal allocation of computing budget to maximize the probability of correct selection in the ordinal optimization setting. This problem has been studied in the literature ...
Limit Theorems for Simulation-based Optimization via
Simulation Theory Model Development: Methodologies Types of Simulation
2015/7/6
This paper develops fundamental theory related to the use of simulation-based non-adaptive random search as a means of optimizing a function that can be expressed as an expectation. Our results establ...
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
large-scale optimization nonlinear programming
2015/7/3
Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective
and constraints.Here w...
A GLOBALLY CONVERGENT LINEARLY CONSTRAINED LAGRANGIAN METHOD FOR NONLINEAR OPTIMIZATION
large-scale optimization nonlinear programming
2015/7/3
The new algorithm has been implemented in Matlab, with an option to use either MINOS or
SNOPT (Fortran codes) to solve the linearly constrained subproblems. Only first derivatives are
required...
PRECONDITIONERS FOR INDEFINITE SYSTEMS ARISING IN OPTIMIZATION
indefinite systems preconditioners linear programming
2015/7/3
Methods are discussed for the solution of sparse linear equations Ky z, where K is
symmetric and indefinite. Since exact solutions are not always required, direct and iterative methods
are both of i...
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...