Minimization methods
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition
... learning methods to a data set with a finite sample ...learning methods are nonparametric ...prediction methods and the development of methods such that the impact of such data points is ...
28
Enhancing the distance minimization methods of matrix updating within a homothetic paradigm
... WSRD methods provide the same loca- tion of zeros in target matrix as in the initial ...the minimization prob- lems (17), (19) and (18), (19) increases rather ...
22
On the use of two L1 norm minimization methods in geodetic networks
... A minimization problem is in the canonical form if all variables are non-negative and all constraints in ...different methods to obtain the solution of a linear programming ...new methods for solving ...
8
On robustness properties of convex risk minimization methods for pattern recognition
... learning methods for the following ...the methods to a data set with a finite sample ...testing methods and the development of methods such that the impact of such data points is ...
20
Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
... the underlying distribution D. The goal of statistical learning is to find a classifier based on the samples and a pre-chosen set F of vector functions with K-components. For this purpose, a very successful method used ...
13
Dictionary Learning for Massive Matrix Factorization
... optimization methods have been developed; unlike classical alternate minimization procedures, they learn matrix decomposi- tions by observing a single matrix column (or row) at each ...
11
Eigenspace-Based Motion Compensation for ISAR Target Imaging
... parametric methods but overcomes their convergence ...entropy minimization methods) this is achieved by estimating a target’s radial motion in order to correct for target scatterer range cell ...
9
Computer Aided Design Model for Optimization Techniques (Newton’s Method)
... unconstrained minimization methods are iterative in nature and hence they start from an initial trial solution and proceed towards the minimum points in sequential ...
5
The forward–backward splitting methods for variational inequalities and minimization problems in Banach spaces
... In [4], the authors prove that every sequence generated by the forward–backward splitting method converges weakly to a solution of the minimization problem if either the penalization function or the objective ...
17
Minimization of Keane’s Bump Function by the Repulsive Particle Swarm and the Differential Evolution Methods
... II. The Objectives of this Paper: We intend in this paper to optimize Keane’s function of different dimensions by the Repulsive Particle Swarm (RPS) and the Differential Evolution (DE) methods of global ...
12
Pulsatile blood flow, shear force, energy dissipation and Murray's Law
... As reviewed by Barakat et al. [4], a causal role for shear stress in determining the radius of an artery is supported by experimental observations. However, much of the evi- dence that supports the SFR hypothesis also ...
10
Ecological and economic justification of the possibility of utilization of weathering gases from gas condensate enterprises on the basis of heat generation
... main methods of research were: system analysis of the fundamental scientific works of Russian and foreign scientists in the field of natural gas transportation and minimization of environmental consequences ...
9
Convergence analysis on a modified generalized alternating direction method of multipliers
... Compared with the traditional proximal approach (12a)–(12d), the semi-proximal terms in (13a)–(13d) are more natural in the sense that the most recently updated values of vari- ables are involved. Actually, the global ...
14
Inertial proximal alternating minimization for nonconvex and nonsmooth problems
... alternating minimization algorithm with inertial ef- fect for the minimization problem of the type L(x, y) = f (x) + R(x, y) + g(y), where f and g are both nonconvex nonsmooth functions, and R is a smooth ...
13
Bundle Methods for Regularized Risk Minimization
... approximation methods such as the Random Feature Map proposed by Rahimi and Recht (2008) can efficiently approximate a infinite dimensional nonlinear feature map associated to a kernel by a finite dimensional ...
55
School of Mathematics, South China University of Technology, China
... The construction goal of the all-English course of Optimization Computation is to integrate the teaching content with the international level, to build a problem-driven discussion-based teaching system that can reflect ...
6
On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
... that confirms that our improved iteration complexity is tight up to a log 2 (p) factor if the largest and smallest eigenvalues of the Hessian matrix do not scale with p. Similar results hold for the CBCM method. We ...
24
Multi step implicit iterative methods with regularization for minimization problems and fixed point problems
... the minimization problem (MP) for a convex and continuously Fréchet differentiable functional and the common fixed point problem of an infinite family of nonexpansive mappings in the setting of Hilbert ...well-known ...
26
Directional distance functions:optimal endogenous directions
... cost minimization and profit maximization using Bayesian methods, we are able to estimate optimal firm-specific directions for each input and output which are consistent with allocative and technical ...
35
Compressive sensing for sparse approximations: constructions, algorithms, and analysis
... rank minimization problem consists of finding the minimum rank matrix in a convex constraint ...rank minimization problem could be solved in polynomial time as long as there were sufficiently many linearly ...
248