Search results
Results from the WOW.Com Content Network
The constrained-optimization problem (COP) is a significant generalization of the classic constraint-satisfaction problem (CSP) model. [1] COP is a CSP that includes an objective function to be optimized. Many algorithms are used to handle the optimization part.
Learning constraints representing these partial evaluation is called graph-based learning. It uses the same rationale of graph-based backjumping. These methods are called "graph-based" because they are based on pairs of variables in the same constraint, which can be found from the graph associated to the constraint satisfaction problem.
A general chance constrained optimization problem can be formulated as follows: (,,) (,,) =, {(,,)}Here, is the objective function, represents the equality constraints, represents the inequality constraints, represents the state variables, represents the control variables, represents the uncertain parameters, and is the confidence level.
The classic model of Constraint Satisfaction Problem defines a model of static, inflexible constraints. This rigid model is a shortcoming that makes it difficult to represent problems easily. [ 33 ] Several modifications of the basic CSP definition have been proposed to adapt the model to a wide variety of problems.
In optimization, robustness features translate into constraints that are parameterized by the uncertain elements of the problem. In the scenario method, [ 1 ] [ 2 ] [ 3 ] a solution is obtained by only looking at a random sample of constraints ( heuristic approach) called scenarios and a deeply-grounded theory tells the user how “robust ...
In mathematics, a constraint is a condition of an optimization problem that the solution must satisfy. There are several types of constraints—primarily equality constraints, inequality constraints, and integer constraints. The set of candidate solutions that satisfy all constraints is called the feasible set. [1]
The adjoint state method is a numerical method for efficiently computing the gradient of a function or operator in a numerical optimization problem. [1] It has applications in geophysics, seismic imaging, photonics and more recently in neural networks. [2] The adjoint state space is chosen to simplify the physical interpretation of equation ...
Constraint propagation in constraint satisfaction problems is a typical example of a refinement model, and formula evaluation in spreadsheets are a typical example of a perturbation model. The refinement model is more general, as it does not restrict variables to have a single value, it can lead to several solutions to the same problem.