109,981 research outputs found
Disentangling Orthogonal Matrices
Motivated by a certain molecular reconstruction methodology in cryo-electron
microscopy, we consider the problem of solving a linear system with two unknown
orthogonal matrices, which is a generalization of the well-known orthogonal
Procrustes problem. We propose an algorithm based on a semi-definite
programming (SDP) relaxation, and give a theoretical guarantee for its
performance. Both theoretically and empirically, the proposed algorithm
performs better than the na\"{i}ve approach of solving the linear system
directly without the orthogonal constraints. We also consider the
generalization to linear systems with more than two unknown orthogonal
matrices
Particulars of Non-Linear Optimization
We are providing a concise introduction to some methods for solving non-linear optimization problems. In mathematics,non-linear programming (NLP) is the process of solving an optimization problem defined by a system of equalities and inequalities, collectively termed constraints, over a set of unknown real variables, along with an objective function to be maximized or minimized, where some of the constraints or the objective function are non-linear. It is the sub-field of mathematical optimization that deals with problems that are not linear. This dissertation conducts its study on the theory that are necessary for understanding and implementing the optimization and an investigation of the algorithms such as Wolfe's Algorithm, Dinkelbach's Algorithm and etc. are available for solving a special class of the non-linear programming problem, quadratic programming problem which is included in the course of study. Optimization problems arise continuously in a wide range of fields such as Power System Control and thus create the need for effective methods of solving them. We discuss the fundamental theory necessary for the understanding of optimization problems, with particular programming problems and the algorithms that solve such problems
A Method for Solving Linear Programming Problems with Unknown Parameters
A method is proposed for solving a problem of linear programming with unknown constraints. The form of the unknown constraints needs to be identified by a proper choice of the observation data. The present method is based upon a bicriterion formulation to the joint identification and optimization problem. A parametric approach is used to obtain an efficient solution to the bicriterion problem. Further, a decomposition into subproblems easily solvable is introduced. The interaction between subproblems is coordinated by an adjustment of a scalar parameter varying over the unit interval
A Global Approach for Solving Edge-Matching Puzzles
We consider apictorial edge-matching puzzles, in which the goal is to arrange
a collection of puzzle pieces with colored edges so that the colors match along
the edges of adjacent pieces. We devise an algebraic representation for this
problem and provide conditions under which it exactly characterizes a puzzle.
Using the new representation, we recast the combinatorial, discrete problem of
solving puzzles as a global, polynomial system of equations with continuous
variables. We further propose new algorithms for generating approximate
solutions to the continuous problem by solving a sequence of convex
relaxations
Conic Optimization Theory: Convexification Techniques and Numerical Algorithms
Optimization is at the core of control theory and appears in several areas of
this field, such as optimal control, distributed control, system
identification, robust control, state estimation, model predictive control and
dynamic programming. The recent advances in various topics of modern
optimization have also been revamping the area of machine learning. Motivated
by the crucial role of optimization theory in the design, analysis, control and
operation of real-world systems, this tutorial paper offers a detailed overview
of some major advances in this area, namely conic optimization and its emerging
applications. First, we discuss the importance of conic optimization in
different areas. Then, we explain seminal results on the design of hierarchies
of convex relaxations for a wide range of nonconvex problems. Finally, we study
different numerical algorithms for large-scale conic optimization problems.Comment: 18 page
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