A sum-of-squares optimization program is an optimization problem with a linear cost function and a particular type of constraint on the decision variables...

Least squares For the "sum of squared differences", see Mean squared error For the "sum of squared error", see Residual sum of squares For the "sum of squares...

form (i.e. a homogeneous polynomial) h(x) of degree 2m in the real n-dimensional vector x is sum of squares of forms (SOS) if and only if there exist forms...

In regression analysis, least squares is a parameter estimation method in which the sum of the squares of the residuals (a residual being the difference...

statistics, sum-minimization problems arise in least squares and in maximum-likelihood estimation (for independent observations). The general class of estimators...

A multivariate polynomial is SOS-convex (or sum of squares convex) if its Hessian matrix H can be factored as H(x) = ST(x)S(x) where S is a matrix (possibly...

networks was introduced. Alternatively, it has been shown that sum-of-squares optimization can yield an approximate polynomial solution to the Hamilton–Jacobi–Bellman...

Quantum optimization algorithms are quantum algorithms that are used to solve optimization problems. Mathematical optimization deals with finding the best...

Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters...

predecessor to PPO, Trust Region Policy Optimization (TRPO), was published in 2015. It addressed the instability issue of another algorithm, the Deep Q-Network...

research topics included computer algebra, sum-of-squares optimization, toric varieties, and dynamical systems of chemical reactions. Gatermann was born on...

programming Sum-of-squares optimization Quadratic programming (see above) Bregman method — row-action method for strictly convex optimization problems Proximal...

rate–distortion optimization which attempts to maintain a similar complexity. The complexity is measured using a combination of sum-of-squares optimization (SSD)...

_{i=1}^{n}w_{i}} . This setting is a special case of stochastic optimization, a well known problem in optimization. In practice, one can perform multiple stochastic...

polynomial Sum-of-squares optimization SOS-convexity Marie-Françoise Roy. The role of Hilbert's problems in real algebraic geometry. Proceedings of the ninth...

squares problems, which is equivalent to minimizing a sum of squared function values. It is an extension of Newton's method for finding a minimum of a...

optimization algorithm, to attempt to find the global minimum of a sum of squares. For details concerning nonlinear data modeling see least squares and...

can be measured with two sums of squares formulas: The sum of squares of residuals, also called the residual sum of squares: S S res = ∑ i ( y i − f i...

matrix — doubly stochastic matrix whose entries are the squares of the absolute values of the entries of some orthogonal matrix Precision matrix — a symmetric...

Turing run-time complexity of the square-root sum problem? More unsolved problems in computer science The square-root sum problem (SRS) is a computational...

In mathematical optimization, constrained optimization (in some contexts called constraint optimization) is the process of optimizing an objective function...

method of iteratively reweighted least squares (IRLS) is used to solve certain optimization problems with objective functions of the form of a p-norm:...

Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting...

Pythagorean triples, sets of three positive integers such that the sum of the squares of the first two equals the square of the third. Each of these triples gives...

Pythagoras' theorem: In a right-angled triangle, the square of the hypotenuse equals the sum of the squares of the other two sides. An easy formula for these...

Lexicographic optimization is a kind of Multi-objective optimization. In general, multi-objective optimization deals with optimization problems with two...

least-squares (DLS) method, is used to solve non-linear least squares problems. These minimization problems arise especially in least squares curve fitting...

The Jenks optimization method, also called the Jenks natural breaks classification method, is a data clustering method designed to determine the best arrangement...

multi-task optimization is that if optimization tasks are related to each other in terms of their optimal solutions or the general characteristics of their...

formulation and process optimization software. TOMLAB – supports global optimization, integer programming, all types of least squares, linear, quadratic,...