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The parameters of the LNP model consist of the linear filters {} and the nonlinearity . The estimation problem (also known as the problem of neural characterization) is the problem of determining these parameters from data consisting of a time-varying stimulus and the set of observed spike times. Techniques for estimating the LNP model ...
Structural optimization problems consist of two levels of optimization task and are commonly referred as mathematical programming problems with equilibrium constraints . The upper level objective in such problems may involve cost minimization or weight minimization subject to bounds on displacements, stresses and contact forces.
Python's Guido van Rossum summarizes C3 superclass linearization thus: [11] Basically, the idea behind C3 is that if you write down all of the ordering rules imposed by inheritance relationships in a complex class hierarchy, the algorithm will determine a monotonic ordering of the classes that satisfies all of them.
In computational complexity theory, Karp's 21 NP-complete problems are a set of computational problems which are NP-complete.In his 1972 paper, "Reducibility Among Combinatorial Problems", [1] Richard Karp used Stephen Cook's 1971 theorem that the boolean satisfiability problem is NP-complete [2] (also called the Cook-Levin theorem) to show that there is a polynomial time many-one reduction ...
spaCy (/ s p eɪ ˈ s iː / spay-SEE) is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. [3] [4] The library is published under the MIT license and its main developers are Matthew Honnibal and Ines Montani, the founders of the software company Explosion.
SOCPs can be solved by interior point methods [2] and in general, can be solved more efficiently than semidefinite programming (SDP) problems. [3] Some engineering applications of SOCP include filter design, antenna array weight design, truss design, and grasping force optimization in robotics. [ 4 ]
The smallest circle problem can be generalized to the smallest ball enclosing a set of balls, [3] to the smallest ball that touches or surrounds each of a set of balls, [4] to the weighted 1-center problem, [5] or to similar smaller enclosing ball problems in non-Euclidean spaces such as the space with distances defined by Bregman divergence. [6]
A linear programming problem is one in which we wish to maximize or minimize a linear objective function of real variables over a polytope.In semidefinite programming, we instead use real-valued vectors and are allowed to take the dot product of vectors; nonnegativity constraints on real variables in LP (linear programming) are replaced by semidefiniteness constraints on matrix variables in ...