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globally optimal
Necessary and sufficient global optimality conditions are proved for problems in which the Pontryagin and Bellman maximum principles do not distinguish between locally and globally optimal processes.
      
Since pseudo-Gray coding is of combinatorial optimization problems which are NP-complete problems, globally optimal solutions are generally impossible.
      
MATLAB-based simulation results showed that POCC can approach the globally optimal solution.
      
Globally optimal quadratic Lyapunov functions for robust stability of systems with structured uncertainty
      
Unlike the conventional neural network, the ACCLN has rich range and flexible dynamics, so that it can be expected to have higher ability of searching for globally optimal or near-optimum results.
      
It illustrates how to employ the primal-dual optimization framework characterizing LS-SVMs in order to derive a globally optimal one-stage estimator for monotone regression.
      
The first, is an approximate, globally optimal minimum-meansquare-error recursive algorithm.
      
It is concluded that nonconvex objective functions also attribute to difficulties in arriving at the globally optimal topology of compliant mechanisms, in addition to the number of design variables and constraints.
      
Globally optimal benchmark solutions to some small-scale discretized continuum topology optimization problems
      
In this note, globally optimal solutions to three sets of small-scale discretized continuum topology optimization problems are presented.
      
In contrast to the heuristic methods considered in many other approaches, our goal is to compute guaranteed globally optimal structures.
      
An algorithm is presented for finding an approximation of a globally optimal solution up to a user-defined accuracy.
      
Under the normality the estimator becomes globally optimal.
      
The globally optimal size also fails to maximize the rate of either fragment distribution or fragment processing within the nest.
      
We then show how our mathematical model can be used to estimate both the globally optimal fitnesses of AR(1) landscapes and their local structure.
      
Avoiding spurious submovement decompositions: a globally optimal algorithm
      
Problem P': find a globallyε-optimal value off and a corresponding point; Problem Q″: find a set of disjoint subintervals of [a, b] containing only points with a globallyε-optimal value and the union of which contains all globally optimal points.
      
A sufficient condition onf is given for the globally optimal points to be in one-to-one correspondance with the obtained intervals.
      
Combining convex duality and a nonconvex duality we can develop a decomposition method to find a globally optimal solution.
      
We will propose a branch and bound algorithm for calculating a globally optimal solution of a portfolio construction/rebalancing problem under concave transaction costs and minimal transaction unit constraints.
      
 

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