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...ethod. While basic hill climbing always chooses the steepest uphill move, "stochastic hill climbing chooses at [[random]] from among the uphill moves; the probab * [[Stochastic gradient descent]] ...

{{Short description|International association of researchers active in optimization}} ...on—including [[mathematics|mathematical theory]], [[:Category:Mathematical optimization software|software implementation]], and [[operations research|practical app ...

{{Short description|Stochastic method of global optimization}} ...l analysis]], '''stochastic tunneling''' (STUN) is an approach to [[global optimization]] based on the [[Monte Carlo method]]-[[Sampling (signal processing)|sampli ...

....edu/record/272200/files/erasmus127.pdf}}</ref> describe their method as a stochastic method involving a combination of sampling, clustering and local search, te ...{cite book | last = Timmer | first = G.T. | title = Global optimization: A stochastic approach | type = Ph.D. Thesis | publisher = Erasmus University Rotterdam ...

...euristic]]. Unlike [[stigmergetic]] communication employed in [[ant colony optimization]], which is based on modification of the physical properties of a simulated ...too long to accomplish. To solve the problem delegates decide to employ a stochastic diffusion search. ...

...e response function. ALOPEX uses a cross-correlation of differences and a stochastic process to overcome this in an attempt to reach the absolute minimum (or ma ...th>\gamma</math>. Further, to find the absolute minimum (or maximum), the stochastic process <math>r_{ij}(n)</math> (Gaussian or other) is added to stochastical ...

{{Short description|Optimization technique in mathematics}} ...function|continuous]] or [[Differentiable function|differentiable]]. Such optimization methods are also known as direct-search, derivative-free, or black-box meth ...

Applied probabilists are particularly concerned with the application of [[stochastic process]]es, and probability more generally, to the natural, applied and so **[[Optimization (computer science)|Optimization]] in [[computer science]] ...

**[[Combinatorial optimization]] *[[Mathematical optimization|Optimization]] ...

* [[Stochastic gradient descent]], an optimization algorithm ...

...nitial value'''. These are necessary for most [[Optimization (mathematics)|optimization]] problems which use [[search algorithms]], because those algorithms are ma ...ermining starting values and optimal values in their own right come from [[stochastic]] methods, the most commonly known of these being [[evolutionary algorithms ...

{{Short description|Monte Carlo method for importance sampling and optimization}} ...able to both [[Combinatorial optimization|combinatorial]] and [[Continuous optimization|continuous]] problems, with either a static or noisy objective. ...

** ''for seminal contributions to the theory and applications of nonlinear optimization over the past several decades.'' ...ibutions over the past several decades, to the theory and applications of “stochastic networks/systems” and their “heavy traffic approximations.”'' ...

{{Short description|Method for problem solving in optimization}} ...stic]] method for solving computationally hard [[Mathematical optimization|optimization]] problems. Local search can be used on problems that can be formulated as ...

**[[Stochastic control]] * [[Particle swarm optimization]] ...

...n of Selection Methods for Evolutionary Optimization |journal=Evolutionary Optimization |date=2000 |url=https://citeseerx.ist.psu.edu/document?doi=d356a3e21bb51d0a ..., tournament selection is often implemented in practice due to its lack of stochastic noise.<ref>{{cite journal|last1=Blickle|first1=Tobias|last2=Thiele|first2=L ...

...rmine a '''new state''' for a system from a previous one. According to its stochastic nature, this new state is accepted at random. Each trial usually counts as ...various generalizations such as the method of [[simulated annealing]] for optimization, in which a fictitious temperature is introduced and then gradually lowered ...

'''Global optimization''' is a branch of [[operations research]], [[applied mathematics]], and [[n ...ary local minimum is relatively straightforward by using classical ''local optimization'' methods. Finding the global minimum of a function is far more difficult: ...

In [[mathematical optimization]] and related fields, '''relaxation''' is a [[mathematical model|modeling s ...complement or supplement [[branch and bound]] algorithms of combinatorial optimization; linear programming and Lagrangian relaxations are used to obtain bounds in ...

...matics]], especially in [[probability]] and [[combinatorics]], a '''doubly stochastic matrix''' ...stochastic matrix is both left [[stochastic matrix|stochastic]] and right stochastic.<ref>{{cite book|last=Marshal, Olkin|title=Inequalities: Theory of Majoriza ...