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Bregman Method
The Bregman method is an iterative algorithm to solve certain convex optimization problems involving regularization. The original version is due to Lev M. Bregman, who published it in 1967. The algorithm is a row-action method accessing constraint functions one by one and the method is particularly suited for large optimization problems where constraints can be efficiently enumerated. The algorithm works particularly well for regularizers such as the \ell_1 norm, where it converges very quickly because of an error cancellation effect. Algorithm In order to be able to use the Bregman method, one must frame the problem of interest as finding \min_u J(u) + f(u), where J is a regularizing function such as \ell_1. The ''Bregman distance'' is defined as D^p(u, v) := J(u) - (J(v) + \langle p, u - v\rangle) where p belongs to the subdifferential of J at u (which we denoted \partial J(u)). One performs the iteration u_:= \min_u(\alpha D(u, u_k) + f(u)), with \alpha a constant to be c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Iterative Algorithm
In computational mathematics, an iterative method is a mathematical procedure that uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the ''n''-th approximation is derived from the previous ones. A specific implementation of an iterative method, including the termination criteria, is an algorithm of the iterative method. An iterative method is called convergent if the corresponding sequence converges for given initial approximations. A mathematically rigorous convergence analysis of an iterative method is usually performed; however, heuristic-based iterative methods are also common. In contrast, direct methods attempt to solve the problem by a finite sequence of operations. In the absence of rounding errors, direct methods would deliver an exact solution (for example, solving a linear system of equations A\mathbf=\mathbf by Gaussian elimination). Iterative methods are often the only choice for nonlinear equations. Howe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Compressed Sensing
Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a signal processing technique for efficiently acquiring and reconstructing a Signal (electronics), signal, by finding solutions to Underdetermined system, underdetermined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Nyquist–Shannon sampling theorem. There are two conditions under which recovery is possible. The first one is sparsity, which requires the signal to be sparse in some domain. The second one is incoherence, which is applied through the isometric property, which is sufficient for sparse signals. Overview A common goal of the engineering field of signal processing is to reconstruct a signal from a series of sampling measurements. In general, this task is impossible because there is no way to reconstruct a signal during the times that the signa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Wirtinger Derivatives
In complex analysis of one and several complex variables, Wirtinger derivatives (sometimes also called Wirtinger operators), named after Wilhelm Wirtinger who introduced them in 1927 in the course of his studies on the theory of functions of several complex variables, are partial differential operators of the first order which behave in a very similar manner to the ordinary derivatives with respect to one real variable, when applied to holomorphic functions, antiholomorphic functions or simply differentiable functions on complex domains. These operators permit the construction of a differential calculus for such functions that is entirely analogous to the ordinary differential calculus for functions of real variables. Historical notes Early days (1899–1911): the work of Henri Poincaré Wirtinger derivatives were used in complex analysis at least as early as in the paper , as briefly noted by and by . As a matter of fact, in the third paragraph of his 1899 paper, Henri Poi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Complex Number
In mathematics, a complex number is an element of a number system that extends the real numbers with a specific element denoted , called the imaginary unit and satisfying the equation i^= -1; every complex number can be expressed in the form a + bi, where and are real numbers. Because no real number satisfies the above equation, was called an imaginary number by René Descartes. For the complex number a+bi, is called the , and is called the . The set of complex numbers is denoted by either of the symbols \mathbb C or . Despite the historical nomenclature "imaginary", complex numbers are regarded in the mathematical sciences as just as "real" as the real numbers and are fundamental in many aspects of the scientific description of the natural world. Complex numbers allow solutions to all polynomial equations, even those that have no solutions in real numbers. More precisely, the fundamental theorem of algebra asserts that every non-constant polynomial equation with real or ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nesterov Method
Nesterov (russian: Не́стеров), until 1938 known by its German name ( lt, Stalupėnai; pl, Stołupiany) and in 1938-1946 as Ebenrode, is a town and the administrative center of Nesterovsky District in Kaliningrad Oblast, Russia, located east of Kaliningrad, near the Russian-Lithuanian border on the railway connecting Kaliningrad Oblast with Moscow. Population figures: History In the Middle Ages, the area in Old Prussia had been settled by the Nadruvian tribe of the Baltic Prussians. It was conquered by the Teutonic Knights in about 1276 and incorporated into the State of the Teutonic Order. From the 15th century onwards, the Knights largely resettled the lands with Samogitian and Lithuanian colonists. Since 1466, it was part of the Kingdom of Poland as a fief held by the Teutonic Order. The settlement itself was first mentioned as ''Stallupoenen'', or ''Stallupönen'', in 1539, named after a nearby river called ''Stalupė'' in Lithuanian. At that time, with the sec ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Limited-memory BFGS
Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory. It is a popular algorithm for parameter estimation in machine learning. The algorithm's target problem is to minimize f(\mathbf) over unconstrained values of the real-vector \mathbf where f is a differentiable scalar function. Like the original BFGS, L-BFGS uses an estimate of the inverse Hessian matrix to steer its search through variable space, but where BFGS stores a dense n\times n approximation to the inverse Hessian (''n'' being the number of variables in the problem), L-BFGS stores only a few vectors that represent the approximation implicitly. Due to its resulting linear memory requirement, the L-BFGS method is particularly well suited for optimization problems with many variables. Instead of the inverse Hessian H''k'', L-BFGS maintains a history of t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Proximal Gradient Method
Proximal gradient methods are a generalized form of projection used to solve non-differentiable convex optimization problems. Many interesting problems can be formulated as convex optimization problems of the form \operatorname\limits_ \sum_^n f_i(x) where f_i: \mathbb^N \rightarrow \mathbb,\ i = 1, \dots, n are possibly non-differentiable convex functions. The lack of differentiability rules out conventional smooth optimization techniques like the steepest descent method and the conjugate gradient method, but proximal gradient methods can be used instead. Proximal gradient methods starts by a splitting step, in which the functions f_1, . . . , f_n are used individually so as to yield an easily implementable algorithm. They are called proximal because each non-differentiable function among f_1, . . . , f_n is involved via its proximity operator. Iterative shrinkage thresholding algorithm, projected Landweber, projected gradient, alternating projections, alternating-d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linear Program
Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. Linear programming is a special case of mathematical programming (also known as mathematical optimization). More formally, linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear inequality constraints. Its feasible region is a convex polytope, which is a set defined as the intersection of finitely many half spaces, each of which is defined by a linear inequality. Its objective function is a real-valued affine (linear) function defined on this polyhedron. A linear programming algorithm finds a point in the polytope where this function has the smallest (or largest) value if such a point exists. Linear programs are problems that can be expressed in canonical form as : \begin & \text && \ma ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dual Ascent Method
Dual or Duals may refer to: Paired/two things * Dual (mathematics), a notion of paired concepts that mirror one another ** Dual (category theory), a formalization of mathematical duality *** see more cases in :Duality theories * Dual (grammatical number), a grammatical category used in some languages * Dual county, a Gaelic games county which in both Gaelic football and hurling * Dual diagnosis, a psychiatric diagnosis of co-occurrence of substance abuse and a mental problem * Dual fertilization, simultaneous application of a P-type and N-type fertilizer * Dual impedance, electrical circuits that are the dual of each other * Dual SIM cellphone supporting use of two SIMs * Aerochute International Dual a two-seat Australian powered parachute design Acronyms and other uses * Dual (brand), a manufacturer of Hifi equipment * DUAL (cognitive architecture), an artificial intelligence design model * DUAL algorithm, or diffusing update algorithm, used to update Internet protocol routing ta ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Method Of Multipliers
Method ( grc, μέθοδος, methodos) literally means a pursuit of knowledge, investigation, mode of prosecuting such inquiry, or system. In recent centuries it more often means a prescribed process for completing a task. It may refer to: *Scientific method, a series of steps, or collection of methods, taken to acquire knowledge *Method (computer programming), a piece of code associated with a class or object to perform a task *Method (patent), under patent law, a protected series of steps or acts *Methodology, comparison or study and critique of individual methods that are used in a given discipline or field of inquiry *''Discourse on the Method'', a philosophical and mathematical treatise by René Descartes * ''Methods'' (journal), a scientific journal covering research on techniques in the experimental biological and medical sciences Arts *Method (music), a kind of textbook to help students learning to play a musical instrument * ''Method'' (2004 film), a 2004 film directed by ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Structural Risk Minimization
Structural risk minimization (SRM) is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be selected from a finite data set, with the consequent problem of overfitting – the model becoming too strongly tailored to the particularities of the training set and generalizing poorly to new data. The SRM principle addresses this problem by balancing the model's complexity against its success at fitting the training data. This principle was first set out in a 1974 paper by Vladimir Vapnik and Alexey Chervonenkis and uses the VC dimension. In practical terms, Structural Risk Minimization is implemented by minimizing E_ + \beta H(W), where E_ is the train error, the function H(W) is called a regularization function, and \beta is a constant. H(W) is chosen such that it takes large values on parameters W that belong to high-capacity subsets of the parameter space. Minimizing H(W) in effect limits the capacity of the accessible subset ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
