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Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives. [1][2] It is generally divided into two subfields: discrete optimization and continuous optimization.
Robust optimization. Robust optimization is a field of mathematical optimization theory that deals with optimization problems in which a certain measure of robustness is sought against uncertainty that can be represented as deterministic variability in the value of the parameters of the problem itself and/or its solution.
Coupon collector's problem. In probability theory, the coupon collector's problem refers to mathematical analysis of "collect all coupons and win" contests. It asks the following question: if each box of a given product (e.g., breakfast cereals) contains a coupon, and there are n different types of coupons, what is the probability that more ...
OptiY is an multidisciplinary design environment, which provides direct and generic interfaces to many CAD/CAE-systems and house-intern codes. Furthermore, a complex COM-interface and a user-node with predefined template are available so that user can self-integrate extern programs for ease of use.
In calculus, Newton's method (also called Newton–Raphson) is an iterative method for finding the roots of a differentiable function , which are solutions to the equation . However, to optimize a twice-differentiable , our goal is to find the roots of . We can therefore use Newton's method on its derivative to find solutions to , also known as ...
Convex optimization. Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently, maximizing concave functions over convex sets).
In mathematical optimization theory, duality or the duality principle is the principle that optimization problems may be viewed from either of two perspectives, the primal problem or the dual problem. If the primal is a minimization problem then the dual is a maximization problem (and vice versa). Any feasible solution to the primal ...
Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. For large numbers of local optima, SA can find the global optimum. [1]
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