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Clarke Generalized Derivative
In mathematics, the Clarke generalized derivatives are types generalized of derivatives that allow for the differentiation of nonsmooth functions. The Clarke derivatives were introduced by Francis Clarke in 1975. Definitions For a locally Lipschitz continuous function f: \mathbb^ \rightarrow \mathbb, the ''Clarke generalized directional derivative'' of f at x \in \mathbb^n in the direction v \in \mathbb^n is defined as f^ (x, v)= \limsup_ \frac, where \limsup denotes the limit supremum. Then, using the above definition of f^, the ''Clarke generalized gradient'' of f at x (also called the ''Clarke subdifferential'') is given as \partial^\! f(x):=\, where \langle \cdot, \cdot\rangle represents an inner product of vectors in \mathbb. Note that the Clarke generalized gradient is set-valued—that is, at each x \in \mathbb^n, the function value \partial^\! f(x) is a set. More generally, given a Banach space X and a subset Y \subset X, the Clarke generalized directional deriva ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Francis Clarke (mathematician)
Frank "Francis" H. Clarke (born 30 July 1948, in Montreal) is a Canadian and French mathematician. Biography Francis Clarke graduated in 1969 from McGill University with a B.Sc. degree in 1969 and in 1973 from the University of Washington with a Ph.D. with thesis advisor R. Tyrrell Rockafellar. In 1978 Clarke became a full professor at the University of British Columbia and gave an invited lecture at the International Congress of Mathematicians (ICM) in Helsinki. In 1984 he was appointed director of the '' Centre de Recherches Mathématiques'' (CRM) of the University of Montreal. During the nine years of his directorship, CRM became Canada's leading national research center for mathematics and its applications. The successes of Clarke's directorship included the creation of workshops and postdoctoral fellowships, thematic years, two series of publications, research awards, and an endowment fund. Francis Clarke is also the founding director of the ''Institut des Sciences Mathémat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Derivative
In mathematics, the derivative of a function of a real variable measures the sensitivity to change of the function value (output value) with respect to a change in its argument (input value). Derivatives are a fundamental tool of calculus. For example, the derivative of the position of a moving object with respect to time is the object's velocity: this measures how quickly the position of the object changes when time advances. The derivative of a function of a single variable at a chosen input value, when it exists, is the slope of the tangent line to the graph of the function at that point. The tangent line is the best linear approximation of the function near that input value. For this reason, the derivative is often described as the "instantaneous rate of change", the ratio of the instantaneous change in the dependent variable to that of the independent variable. Derivatives can be generalized to functions of several real variables. In this generalization, the deriv ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lipschitz Continuity
In mathematical analysis, Lipschitz continuity, named after German mathematician Rudolf Lipschitz, is a strong form of uniform continuity for functions. Intuitively, a Lipschitz continuous function is limited in how fast it can change: there exists a real number such that, for every pair of points on the graph of this function, the absolute value of the slope of the line connecting them is not greater than this real number; the smallest such bound is called the ''Lipschitz constant'' of the function (or '' modulus of uniform continuity''). For instance, every function that has bounded first derivatives is Lipschitz continuous. In the theory of differential equations, Lipschitz continuity is the central condition of the Picard–Lindelöf theorem which guarantees the existence and uniqueness of the solution to an initial value problem. A special type of Lipschitz continuity, called contraction, is used in the Banach fixed-point theorem. We have the following chain of str ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Limit Supremum
In mathematics, the limit inferior and limit superior of a sequence can be thought of as limiting (that is, eventual and extreme) bounds on the sequence. They can be thought of in a similar fashion for a Function (mathematics), function (see limit of a function). For a set, they are the infimum and supremum of the set's limit points, respectively. In general, when there are multiple objects around which a sequence, function, or set accumulates, the inferior and superior limits extract the smallest and largest of them; the type of object and the measure of size is context-dependent, but the notion of extreme limits is invariant. Limit inferior is also called infimum limit, limit infimum, liminf, inferior limit, lower limit, or inner limit; limit superior is also known as supremum limit, limit supremum, limsup, superior limit, upper limit, or outer limit. The limit inferior of a sequence x_n is denoted by \liminf_x_n\quad\text\quad \varliminf_x_n. The limit superior of a sequence ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Subdifferential
In mathematics, the subderivative, subgradient, and subdifferential generalize the derivative to convex functions which are not necessarily differentiable. Subderivatives arise in convex analysis, the study of convex functions, often in connection to convex optimization. Let f:I \to \mathbb be a real-valued convex function defined on an open interval of the real line. Such a function need not be differentiable at all points: For example, the absolute value function ''f''(''x'')=, ''x'', is nondifferentiable when ''x''=0. However, as seen in the graph on the right (where ''f(x)'' in blue has non-differentiable kinks similar to the absolute value function), for any ''x''0 in the domain of the function one can draw a line which goes through the point (''x''0, ''f''(''x''0)) and which is everywhere either touching or below the graph of ''f''. The slope of such a line is called a ''subderivative'' (because the line is under the graph of ''f''). Definition Rigorously, a ''subd ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Inner Product
In mathematics, an inner product space (or, rarely, a Hausdorff pre-Hilbert space) is a real vector space or a complex vector space with an operation called an inner product. The inner product of two vectors in the space is a scalar, often denoted with angle brackets such as in \langle a, b \rangle. Inner products allow formal definitions of intuitive geometric notions, such as lengths, angles, and orthogonality (zero inner product) of vectors. Inner product spaces generalize Euclidean vector spaces, in which the inner product is the dot product or ''scalar product'' of Cartesian coordinates. Inner product spaces of infinite dimension are widely used in functional analysis. Inner product spaces over the field of complex numbers are sometimes referred to as unitary spaces. The first usage of the concept of a vector space with an inner product is due to Giuseppe Peano, in 1898. An inner product naturally induces an associated norm, (denoted , x, and , y, in the picture); ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Subgradient Method
Subgradient methods are iterative methods for solving convex minimization problems. Originally developed by Naum Z. Shor and others in the 1960s and 1970s, subgradient methods are convergent when applied even to a non-differentiable objective function. When the objective function is differentiable, sub-gradient methods for unconstrained problems use the same search direction as the method of steepest descent. Subgradient methods are slower than Newton's method when applied to minimize twice continuously differentiable convex functions. However, Newton's method fails to converge on problems that have non-differentiable kinks. In recent years, some interior-point methods have been suggested for convex minimization problems, but subgradient projection methods and related bundle methods of descent remain competitive. For convex minimization problems with very large number of dimensions, subgradient-projection methods are suitable, because they require little storage. Subgradient proj ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Subderivative
In mathematics, the subderivative, subgradient, and subdifferential generalize the derivative to convex functions which are not necessarily differentiable. Subderivatives arise in convex analysis, the study of convex functions, often in connection to convex optimization. Let f:I \to \mathbb be a real-valued convex function defined on an open interval of the real line. Such a function need not be differentiable at all points: For example, the absolute value function ''f''(''x'')=, ''x'', is nondifferentiable when ''x''=0. However, as seen in the graph on the right (where ''f(x)'' in blue has non-differentiable kinks similar to the absolute value function), for any ''x''0 in the domain of the function one can draw a line which goes through the point (''x''0, ''f''(''x''0)) and which is everywhere either touching or below the graph of ''f''. The slope of such a line is called a ''subderivative'' (because the line is under the graph of ''f''). Definition Rigorously, a ''subderivat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Generalizations Of The Derivative
In mathematics, the derivative is a fundamental construction of differential calculus and admits many possible generalizations within the fields of mathematical analysis, combinatorics, algebra, geometry, etc. Fréchet derivative The Fréchet derivative defines the derivative for general normed vector spaces V, W. Briefly, a function f : U \to W, U an open subset of V, is called ''Fréchet differentiable'' at x \in U if there exists a bounded linear operator A:V\to W such that \lim_ \frac = 0. Functions are defined as being differentiable in some open neighbourhood of x, rather than at individual points, as not doing so tends to lead to many pathological counterexamples. The Fréchet derivative is quite similar to the formula for the derivative found in elementary one-variable calculus, \lim_\frac = A, and simply moves ''A'' to the left hand side. However, the Fréchet derivative ''A'' denotes the function t \mapsto f'(x) \cdot t. In multivariable calculus, in the cont ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
