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Regulation of algorithms, or algorithmic regulation, is the creation of laws, rules and public sector policies for promotion and regulation of algorithms, particularly in artificial intelligence and machine learning. [1] [2] [3] For the subset of AI algorithms, the term regulation of artificial intelligence is used.
Algorithmic regulation may refer to: Government by algorithm, use of algorithms in government; Regulation of algorithms, rules and laws for algorithms
Regulation is now generally considered necessary to both encourage AI and manage associated risks. [19] [20] [21] Public administration and policy considerations generally focus on the technical and economic implications and on trustworthy and human-centered AI systems, [22] although regulation of artificial superintelligences is also ...
Information-based complexity (IBC) studies optimal algorithms and computational complexity for continuous problems. IBC has studied continuous problems as path integration, partial differential equations, systems of ordinary differential equations, nonlinear equations, integral equations, fixed points, and very-high-dimensional integration.
Nonlinear control theory – This covers a wider class of systems that do not obey the superposition principle, and applies to more real-world systems because all real control systems are nonlinear. These systems are often governed by nonlinear differential equations. The few mathematical techniques which have been developed to handle them are ...
Linear-quadratic regulator — system dynamics is a linear differential equation, objective is quadratic; Linear-quadratic-Gaussian control (LQG) — system dynamics is a linear SDE with additive noise, objective is quadratic Optimal projection equations — method for reducing dimension of LQG control problem
The case where the system dynamics are described by a set of linear differential equations and the cost is described by a quadratic function is called the LQ problem. One of the main results in the theory is that the solution is provided by the linear–quadratic regulator (LQR), a feedback controller whose equations are given below.
A simple form of regularization applied to integral equations (Tikhonov regularization) is essentially a trade-off between fitting the data and reducing a norm of the solution. More recently, non-linear regularization methods, including total variation regularization , have become popular.