Examples of using Optimization problem in English and their translations into Spanish
{-}
-
Colloquial
-
Official
Quantile regression is a nondifferentiable convex optimization problem.
Solver-Based Optimization Problem Setup describes the solver-based approach.
To achieve this objective, first the optimization problem is formulated.
The optimization problem was solved by an exhaustive search approach.
Model a design ordecision problem as an optimization problem.
So any optimization problem, which has a linear objective function, either.
Distinguish between decision variables and parameters of an optimization problem.
For optimization problems there is a more specific classification of algorithms;
The simplest way to solve this optimization problem is by the method of substitution.
Huffman Tree, Kruskal, Prim,Sollin are greedy algorithms that can solve this optimization problem.
This constrained optimization problem can be solved by first using the constraint to substitute for.
Robust programming is,like stochastic programming, an attempt to capture uncertainty in the data underlying the optimization problem.
Often a learning-to-rank problem is reformulated as an optimization problem with respect to one of these metrics.
First, any optimization problem has some objective: minimizing travel time, minimizing cost, maximizing profits, maximizing utility, etc.
In computer science andoperations research, exact algorithms are algorithms that always solve an optimization problem to optimality.
In the set covering optimization problem, the input is a pair( U, S){\displaystyle({\mathcal{U}},{\mathcal{S}})}, and the task is to find a set covering that uses the fewest sets.
In a genetic algorithm, a population of candidate solutions(called individuals, creatures,or phenotypes) to an optimization problem is evolved toward better solutions.
In the set packing optimization problem, the input is a pair( U, S){\displaystyle({\mathcal{U}},{\mathcal{S}})}, and the task is to find a set packing that uses the most sets.
Standard techniques for the solution of difference or differential equations can then be used to calculate the dynamics of the state variables andthe control variables of the optimization problem.
In a nutshell,a calibration is an optimization problem where we search for a set of parameter values that maximize the value of a utility function i.e.
Recently, an evolutionary multiobjective optimization(EMO) approach was proposed,in which a suitable second objective is added to the originally single objective multimodal optimization problem, so that the multiple solutions form a weak pareto-optimal front.
An optimization problem can be represented in the following way Given: a function f: A→{\displaystyle\to} R from some set A to the real numbers Search for: an element x0 in A such that f(x0)≤ f(x) for all x in A"minimization.
UNTIL(requirements are met) In ABC, a population based algorithm,the position of a food source represents a possible solution to the optimization problem and the nectar amount of a food source corresponds to the quality(fitness) of the associated solution.
Such a formulation is called an optimization problem or a mathematical programming problem a term not directly related to computer programming, but still in use for example in linear programming- see History below.
Another approach for multi model fitting is known as PEARL, which combines model sampling from data points as in RANSAC with iterative re-estimation of inliers andthe multi-model fitting being formulated as an optimization problem with a global energy functional describing the quality of the overall solution.
Lead a group of students to obtain the best solution to an optimization problem or exercise of analysis given, considering the solutions of the rest of his team members and after a discussion among all team members.
In order to solve the optimization problem, the EOBLi project uses advanced multi-objective modeling techniques which, aided by computer intelligence algorithms, allow to create optimal ion-lithium battery bank designs.
The similarity of subsequent problems is even further exploited by path following algorithms(or"real-time iterations")that never attempt to iterate any optimization problem to convergence, but instead only take a few iterations towards the solution of the most current NMPC problem, before proceeding to the next one, which is suitably initialized; see, e.g.
Universal constructions are more general than adjoint functors:a universal construction is like an optimization problem; it gives rise to an adjoint pair if and only if this problem has a solution for every object of D equivalently, every object of C.
When the number of clusters is fixed to k,k-means clustering gives a formal definition as an optimization problem: find the k cluster centers and assign the objects to the nearest cluster center, such that the squared distances from the cluster are minimized.