PPT-Sum of squares optimization:

scalability improvements and applications to difference of convex programming Georgina Hall Princeton ORFE Joint work with Amir Ali Ahmadi Princeton ORFE 1 Nonnegative polynomials. By scattered data we mean an arbitrary set of points in which carry scalar quantities ie a scalar 64257eld in dimensional parameter space In contrast to the global nature of the leastsquares 64257t the weighted local ap proximation is computed eithe 1 Weighted Least Squares as a Solution to Heteroskedasticity 5 3 Local Linear Regression 10 4 Exercises 15 1 Weighted Least Squares Instead of minimizing the residual sum of squares RSS 1 x 1 we could minimize the weighted sum of squares WSS 946 Ingredients 2 cups sugar2 squares unsweetened chocolate1 cup whole milk Sum & Difference of Two Cubes. Recognizing Perfect Squares . Difference of Two Squares. Recognizing Perfect Cubes. Sum of Two Cubes. Difference of Two Cubes. 5.6. 1. Recognizing Perfect Squares (X). Least Squares. Method. of . Least. . Squares. :. Deterministic. . approach. . The. . inputs. u(1), u(2), ..., u(N) . are. . applied. . to. . the. . system. The. . outputs. y(1), y(2), ..., y(N) . Adaptive Filters. Definition. With the arrival of new data samples estimates are updated recursively.. Introduce a weighting factor to the sum-of-error-squares definition. Weighting factor. Forgetting factor. scalability . improvements . and . applications . to . difference . of convex programming.. Georgina . Hall. Princeton, . ORFE. Joint work with . Amir Ali Ahmadi. Princeton, ORFE. 1. Nonnegative polynomials. scalability . improvements . and . applications . to . difference . of convex programming.. Georgina . Hall. Princeton, . ORFE. Joint work with . Amir Ali Ahmadi. Princeton, ORFE. 1. Nonnegative polynomials. ANOVA Terms — Sums of Squares. S.S.—The sum of squared deviations of each data point from some mean value. Between groups—The difference between S.S. combined and S.S. within . groups. [variability due to IV]. Poker Squares, Word Squares, and Take It Easy!. Todd W. Neller. Outline. Learn and play Poker Squares. Generalize game. concepts (sequential. placement optimization games). Learn and play two closely related games:. cards, letters, and hexes. Todd W. Neller. Outline. Learn and play Poker Squares. Generalize game. concepts. Learn and play two closely related games:. Word Squares. Take it. Easy!. Future Human vs. Machine Poker Squares Competition?. How many squares are in the border? Share Your Way of Counting One student at a time, explain how you counted. Come to the board if it’s helpful. Does everyone else understand? Does someone else have another way? Photo by Carl Warner. Feature Matching and Robust Fitting. Computer Vision. James Hays. Acknowledgment: Many . slides from Derek . Hoiem. and . Grauman&Leibe. . 2008 AAAI Tutorial. Read . Szeliski. Matthew Heintzelman. EECS 800 SAR Study Project . ‹#›. . Background:. Typical SAR image formation . algorithms. produce relatively high sidelobes (fast-time and slow-time) that . contribute. to image speckle and can mask scatterers with a low RCS..