convex optimizationmax-functionExamples of the Problem In a classic nonlinear program (NLP) a smooth objective function is minimized on a feasible set defined by finitely many smooth constraints. However, many optimization problems...Claudia Sagastizábal...
meshop.optimize<Meshmesh><floatnormalThreshold><floatedgeThreshold><floatbias><floatmaxEdge>saveMatBoundries:<boolean=true>saveSmoothBoundries:<boolean=true>autoEdge:<boolean=true> Reduces the mesh in complexity by reducing the number of faces based on a surface normal threshold. Adjacent faces whose ...
When off, will smooth based on auto smooth value. This distinction allows the user to set Autosmooth 0 to get a default smoothing that gives usable selection modes from smoothing groups while keeping a mainly hard-edge object. * Adds new Face Smoothing Selection functions in Selection rollout ...
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7.3Polynomial Activation Functions Smooth Adaptive AF (SAAF) is defined as the piecewise polynomial function[105]. Two power functions symmetric to the linear part of ReLU are combined in[106]to improve the performance of ReLU. A piecewise polynomial approximation based AF is also learnt from the...
Population games are games with a finite set of available strategies and an infinite number of players, in which the reward for choosing a given strategy i
Filter your target objects by: number of polygons; size; static/animated. Full support for animated objects and complex rigged characters. Custom options for Polygon Reduction and Animation Baking. Choose between 5 types of proxy mesh: from source, convex hull, vertex cloud, box, custom mesh. ...
We investigate the advantages of the proposed approach for problems where the objective function is not limited to smooth strongly convex--concave functions. The effectiveness of the proposed approach is demonstrated in the robust berthing control problem with uncertainty.ngly convex--concave functions....
Nesterov, Y.: Smooth minimization of nonsmooth functions. Technical report, CORE DP (2003) Google Scholar Nesterov, Y., Nemirovski, A.: Interior-point polynomial algorithms in convex programming. SIAM Studies in Applied Mathematics, vol. 13. Society for Industrial and Applied Mathematics (SIAM)...
However, this risk is rarely directly minimized, notably because of (1) its lack of smoothness (and even continuity) and (2) its lack of convexity. The classical approach consists in minimizing instead a convex (and possibly smooth) surrogate, such as the hinge loss (used in Support Vector...