'Quadratic approximation' diaporamas de présentation

A Method for Determining Turbine Airfoil Geometry Parameters from a Set of Coordinates

369 views • 27 slides

Kernel Learning with a Million Kernels

Kernel Learning with a Million Kernels. Ashesh Jain IIT Delhi. S VN Vishwanathan Purdue University. Manik Varma Microsoft Research India. To appear SIGKDD 2012. The objective in kernel learning is to jointly learn both SVM and kernel parameters from training data.

407 views • 18 slides

Ph. Bekaert, M. Sbert, Y Willems Department of Computer Science, K.U.Leuven I.M.A., U.d.Girona

The Computation of Higher-Order Radiosity Approximations with a Stochastic Jacobi Iterative Method. Ph. Bekaert, M. Sbert, Y Willems Department of Computer Science, K.U.Leuven I.M.A., U.d.Girona. The Radiosity Method. Reflectivity. Self-emitted radiosity. Total radiosity. Form factor

262 views • 17 slides

Starting problems

Starting problems. Write a tangent line approximation for each: g(x) = 3x 2 + 2x + 1 at x = 0 f(x) = cos x at x = 0 Write the formula for finding a tangent line approximation for function f at x=0. A closer look .

187 views • 11 slides

114 views • 11 slides

Part 4 Nonlinear Programming

Part 4 Nonlinear Programming. 4.6 Successive Quadratic Programming (SQP). Direct Quadratic Approximation. Step 1 : Formulate the QP problem. Necessary Conditions of NLP. Sufficient Conditions of NLP. Quadratic Approximation of the Lagrangian Function. Claim 1. Claim 2.

133 views • 10 slides