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...at.uc.pt/~lnv/idfo/|accessdate=2014-01-18 |series= MPS-SIAM Book Series on Optimization| publisher=SIAM|location=Philadelphia}}</ref> ...s when <math>f</math> is multi-modal, in which case local derivative-based methods only give local optima, but might miss the global one. ...

...tial locations where the fireworks will explode. Then the amount of sparks and their locations are determined based on the proximity of the firework to th [[Category:Optimization algorithms and methods]] ...

...It is commonly employed to evaluate the performance of global optimization algorithms. The function is defined<ref>Griewank, A. O. "Generalized Descent for Global Optimization." J. Opt. Th. Appl. 34, 11–39, 1981</ref> as: ...

...odoulos Floudas|title=Deterministic global optimization : theory, methods, and applications|year=2000|publisher=Kluwer Academic Publ.|location=Dordrecht [ ...h>B_y = \{x \in X: (x,y) \in B\}</math> is a convex set in <math>X </math> and for every fixed <math>x\in X </math>, <math>B_x = \{y \in Y: (x,y) \in B\}< ...

...ent''' is an [[Iterative algorithm|iterative]] [[Mathematical optimization|optimization]] [[algorithm]] for finding a [[local minimum]] of a [[differentiable funct It generalizes algorithms such as [[gradient descent]] and [[Multiplicative weight update method|multiplicative weights]]. ...

| book-title = Genetic and Evolutionary Computation Conference (GECCO) ...inate descent]] [[algorithm]] to non-separable [[Mathematical optimization|optimization]] by the use of [[adaptive encoding]].<ref> ...

{{Short description|Optimization algorithm}} ...øe, M. P., & Sigmund, O. (2003). ''Topology optimization: theory, methods, and applications''. Berlin: Springer.</ref> ...

...s problem derives a better lower bound than HF{{What|date=November 2021}}, and propose a quadratic-time algorithm from calculating the bound. It is known [[Category:Optimization algorithms and methods]] ...

...a generalized form of projection used to solve non-differentiable [[convex optimization]] problems. ...comparison between the iterates of the projected gradient method (in red) and the [[Frank–Wolfe algorithm|Frank-Wolfe method]] (in green).]] ...

...tial quadratic programming]] (SQP), SLQP proceeds by solving a sequence of optimization subproblems. The difference between the two approaches is that: ...SLQP suitable to large-scale optimization problems, for which efficient LP and EQP solvers are available, these problems being easier to scale than full-f ...

...on between the trust region boundary and the line joining the Cauchy point and the Gauss-Newton step (dog leg step).<ref name="yuan">Yuan (2000)</ref> ...construction of the dog leg step and the shape of a [[Golf course#Fairway and rough|dogleg hole]] in [[golf]].<ref name="yuan"/> ...

...nes]].<ref name="Lohne">{{cite book|title=Vector Optimization with Infimum and Supremum|author=Andreas Löhne|publisher=Springer|year=2011|isbn=97836421835 ...a polyhedral convex ordering cone <math>C</math> having nonempty interior and containing no lines. The feasible set is <math>S=\{x \in \mathbb{R}^n:\; A ...

...os Leal| F. Santos]]| year = 2010 | title = Triangulations: Structures and Algorithms | publisher = [[Springer-Verlag]] | edition = 2nd revised | isbn=9783642129 ...tion | author = [[Mark de Berg]], [[Marc van Kreveld]], [[Mark Overmars]], and [[Otfried Schwarzkopf]] | year = 2000 | title = Computational Geometry | pu ...

...e"/> [[integer programming]],<ref name="fan10scoip"/> and [[combinatorial optimization]] problems. It has been incorporated into the [https://wiki.openoffice.org/ ...space <math>S</math>. In SCO, each state is called a ''knowledge point'', and the function <math>f</math> is the ''goodness function''. ...

|class=[[Optimization algorithm]] for training support vector machines '''Sequential minimal optimization''' ('''SMO''') is an algorithm for solving the [[quadratic programming]] (Q ...

...\lambda</math> is the regularization parameter trading off signal fidelity and simplicity. The simplicity is here measured using the sparsity of the solut ...(pp. 801-808). [https://papers.nips.cc/paper/2979-efficient-sparse-coding-algorithms.pdf]</ref> where the features are included based on the estimate of their s ...

...MM itself is not an algorithm, but a description of how to construct an [[optimization algorithm]]. |title=MM Optimization Algorithms ...

...[Ansys]].<ref>https://www.ansys.com/about-ansys/news-center/10-24-19-ansys-and-dynardo-sign-definitive-acquisition-agreement</ref> ...of the model responses. In contrast to local derivative based sensitivity methods, the variance based approach quantifies the contribution with respect to th ...

{{short description|Computational method used to solve optimization problems of different types}} ...mpetitive algorithms''' are a type of computational method used to solve [[optimization problem]]s of different types.<ref name=ica_en_2007_cnf_atashpaz_ica_ica>{{ ...

{{Short description|Function in mathematical optimization}} ...d to be ''proper'' if it is not identically equal to <math>+\infty</math>, and <math>-\infty</math> is not in its image.</ref> [[Semi-continuity|lower sem ...