1,741 research outputs found
Structural Optimization Using the Newton Modified Barrier Method
The Newton Modified Barrier Method (NMBM) is applied to structural optimization problems with large a number of design variables and constraints. This nonlinear mathematical programming algorithm was based on the Modified Barrier Function (MBF) theory and the Newton method for unconstrained optimization. The distinctive feature of the NMBM method is the rate of convergence that is due to the fact that the design remains in the Newton area after each Lagrange multiplier update. This convergence characteristic is illustrated by application to structural problems with a varying number of design variables and constraints. The results are compared with those obtained by optimality criteria (OC) methods and by the ASTROS program
On representations of the feasible set in convex optimization
We consider the convex optimization problem where is convex, the feasible set K is convex and Slater's
condition holds, but the functions are not necessarily convex. We show
that for any representation of K that satisfies a mild nondegeneracy
assumption, every minimizer is a Karush-Kuhn-Tucker (KKT) point and conversely
every KKT point is a minimizer. That is, the KKT optimality conditions are
necessary and sufficient as in convex programming where one assumes that the
are convex. So in convex optimization, and as far as one is concerned
with KKT points, what really matters is the geometry of K and not so much its
representation.Comment: to appear in Optimization Letter
Fast Primal-Dual Gradient Method for Strongly Convex Minimization Problems with Linear Constraints
In this paper we consider a class of optimization problems with a strongly
convex objective function and the feasible set given by an intersection of a
simple convex set with a set given by a number of linear equality and
inequality constraints. A number of optimization problems in applications can
be stated in this form, examples being the entropy-linear programming, the
ridge regression, the elastic net, the regularized optimal transport, etc. We
extend the Fast Gradient Method applied to the dual problem in order to make it
primal-dual so that it allows not only to solve the dual problem, but also to
construct nearly optimal and nearly feasible solution of the primal problem. We
also prove a theorem about the convergence rate for the proposed algorithm in
terms of the objective function and the linear constraints infeasibility.Comment: Submitted for DOOR 201
Sums over Graphs and Integration over Discrete Groupoids
We show that sums over graphs such as appear in the theory of Feynman
diagrams can be seen as integrals over discrete groupoids. From this point of
view, basic combinatorial formulas of the theory of Feynman diagrams can be
interpreted as pull-back or push-forward formulas for integrals over suitable
groupoids.Comment: 27 pages, 4 eps figures; LaTeX2e; uses Xy-Pic. Some ambiguities
fixed, and several proofs simplifie
Advances in low-memory subgradient optimization
One of the main goals in the development of non-smooth optimization is to cope with high dimensional problems by decomposition, duality or Lagrangian relaxation which greatly reduces the number of variables at the cost of worsening differentiability of objective or constraints. Small or medium dimensionality of resulting non-smooth problems allows to use bundle-type algorithms to achieve higher rates of convergence and obtain higher accuracy, which of course came at the cost of additional memory requirements, typically of the order of n2, where n is the number of variables of non-smooth problem. However with the rapid development of more and more sophisticated models in industry, economy, finance, et all such memory requirements are becoming too hard to satisfy. It raised the interest in subgradient-based low-memory algorithms and later developments in this area significantly improved over their early variants still preserving O(n) memory requirements. To review these developments this chapter is devoted to the black-box subgradient algorithms with the minimal requirements for the storage of auxiliary results, which are necessary to execute these algorithms. To provide historical perspective this survey starts with the original result of N.Z. Shor which opened this field with the application to the classical transportation problem. The theoretical complexity bounds for smooth and non-smooth convex and quasi-convex optimization problems are briefly exposed in what follows to introduce to the relevant fundamentals of non-smooth optimization. Special attention in this section is given to the adaptive step-size policy which aims to attain lowest complexity bounds. Unfortunately the non-differentiability of objective function in convex optimization essentially slows down the theoretical low bounds for the rate of convergence in subgradient optimization compared to the smooth case but there are different modern techniques that allow to solve non-smooth convex optimization problems faster then dictate lower complexity bounds. In this work the particular attention is given to Nesterov smoothing technique, Nesterov Universal approach, and Legendre (saddle point) representation approach. The new results on Universal Mirror Prox algorithms represent the original parts of the survey. To demonstrate application of non-smooth convex optimization algorithms for solution of huge-scale extremal problems we consider convex optimization problems with non-smooth functional constraints and propose two adaptive Mirror Descent methods. The first method is of primal-dual variety and proved to be optimal in terms of lower oracle bounds for the class of Lipschitz-continuous convex objective and constraints. The advantages of application of this method to sparse Truss Topology Design problem are discussed in certain details. The second method can be applied for solution of convex and quasi-convex optimization problems and is optimal in a sense of complexity bounds. The conclusion part of the survey contains the important references that characterize recent developments of non-smooth convex optimization
Molecular-orbital-free algorithm for excited states in time-dependent perturbation theory
A non-linear conjugate gradient optimization scheme is used to obtain
excitation energies within the Random Phase Approximation (RPA). The solutions
to the RPA eigenvalue equation are located through a variational
characterization using a modified Thouless functional, which is based upon an
asymmetric Rayleigh quotient, in an orthogonalized atomic orbital
representation. In this way, the computational bottleneck of calculating
molecular orbitals is avoided. The variational space is reduced to the
physically-relevant transitions by projections. The feasibility of an RPA
implementation scaling linearly with system size, N, is investigated by
monitoring convergence behavior with respect to the quality of initial guess
and sensitivity to noise under thresholding, both for well- and ill-conditioned
problems. The molecular- orbital-free algorithm is found to be robust and
computationally efficient providing a first step toward a large-scale, reduced
complexity calculation of time-dependent optical properties and linear
response. The algorithm is extensible to other forms of time-dependent
perturbation theory including, but not limited to, time-dependent Density
Functional theory.Comment: 9 pages, 7 figure
The Hopf Algebra of Renormalization, Normal Coordinates and Kontsevich Deformation Quantization
Using normal coordinates in a Poincar\'e-Birkhoff-Witt basis for the Hopf
algebra of renormalization in perturbative quantum field theory, we investigate
the relation between the twisted antipode axiom in that formalism, the Birkhoff
algebraic decomposition and the universal formula of Kontsevich for quantum
deformation.Comment: 21 pages, 15 figure
The Pure Virtual Braid Group Is Quadratic
If an augmented algebra K over Q is filtered by powers of its augmentation
ideal I, the associated graded algebra grK need not in general be quadratic:
although it is generated in degree 1, its relations may not be generated by
homogeneous relations of degree 2. In this paper we give a sufficient criterion
(called the PVH Criterion) for grK to be quadratic. When K is the group algebra
of a group G, quadraticity is known to be equivalent to the existence of a (not
necessarily homomorphic) universal finite type invariant for G. Thus the PVH
Criterion also implies the existence of such a universal finite type invariant
for the group G. We apply the PVH Criterion to the group algebra of the pure
virtual braid group (also known as the quasi-triangular group), and show that
the corresponding associated graded algebra is quadratic, and hence that these
groups have a (not necessarily homomorphic) universal finite type invariant.Comment: 53 pages, 15 figures. Some clarifications added and inaccuracies
corrected, reflecting suggestions made by the referee of the published
version of the pape
Gradient methods for problems with inexact model of the objective
We consider optimization methods for convex minimization problems under inexact information on the objective function. We introduce inexact model of the objective, which as a particular cases includes inexact oracle [19] and relative smoothness condition [43]. We analyze gradient method which uses this inexact model and obtain convergence rates for convex and strongly convex problems. To show potential applications of our general framework we consider three particular problems. The first one is clustering by electorial model introduced in [49]. The second one is approximating optimal transport distance, for which we propose a Proximal Sinkhorn algorithm. The third one is devoted to approximating optimal transport barycenter and we propose a Proximal Iterative Bregman Projections algorithm. We also illustrate the practical performance of our algorithms by numerical experiments
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