Infinite positive-definite quadratic programming in a Hilbert space

1996 ◽  
Vol 88 (3) ◽  
pp. 743-749 ◽  
Author(s):  
J. Semple
Keyword(s):  
Hilbert Space ◽  
Quadratic Programming ◽  
Positive Definite

A polynomial algorithm for the quadratic programming problem

2014 ◽  
Vol 29 (3) ◽  
Author(s):  
Valentin A. Bereznev
Keyword(s):  
Computational Complexity ◽  
Quadratic Form ◽  
Quadratic Programming ◽  
Programming Problem ◽  
Polynomial Complexity ◽  
Polynomial Algorithm ◽  
Positive Definite Matrix ◽  
Positive Definite ◽  
Quadratic Programming Problem ◽  
Convex Polyhedral Cone

AbstractAn approach based on projection of a vector onto a pointed convex polyhedral cone is proposed for solving the quadratic programming problem with a positive definite matrix of the quadratic form. It is proved that this method has polynomial complexity. A method is said to be of polynomial computational complexity if the solution to the problem can be obtained in N

Positive Definite Sequence of Operators and a Fixed Point Theorem

1972 ◽  
Vol 15 (2) ◽  
pp. 295-295
Author(s):  
A. T. Dash
Keyword(s):  
Hilbert Space ◽  
Fixed Point ◽  
Fixed Point Theorem ◽  
Point Theorem ◽  
Positive Definite ◽  
Definite Sequence ◽  
Positive Definite Sequence ◽  
Image Position

The purpose of this note is to prove the following:Theorem. Let {An} be a positive definite sequence of operators on a Hilbert space H with A0=1. If A1f=f for some f in H, then Anf=f for all n.Note that a bilateral sequence of operators {An:n = 0, ±1, ±2,…} on H is positive definite iffor every finitely nonzero sequence {fn} of vectors in H [1].

Inequalities between means of positive operators

1978 ◽  
Vol 83 (3) ◽  
pp. 393-401 ◽  
Author(s):  
K. V. Bhagwat ◽  
R. Subramanian
Keyword(s):  
Hilbert Space ◽  
Dimensional Space ◽  
Adjoint Operator ◽  
Positive Definite ◽  
Inner Product ◽  
Finite Dimensional Space ◽  
Finite Dimensional ◽  
Definite Operator ◽  
Unit Operator ◽  
Adjoint Operators

One of the most fruitful – and natural – ways of introducing a partial order in the set of bounded self-adjoint operators in a Hilbert space is through the concept of a positive operator. A bounded self-adjoint operator A denned on is called positive – and one writes A ≥ 0 - if the inner product (ψ, Aψ) ≥ 0 for every ψ ∈ . If, in addition, (ψ, Aψ) = 0 only if ψ = 0, then A is called positive-definite and one writes A > 0. Further, if there exists a real number γ > 0 such that A — γI ≥ 0, I being the unit operator, then A is called strictly positive (in symbols, A ≫ 0). In a finite dimensional space, a positive-definite operator is also strictly positive.

scholarly journals Correct selfadjoint and positive extensions of nondensely defined minimal symmetric operators

2005 ◽  
Vol 2005 (7) ◽  
pp. 767-790 ◽  
Author(s):  
I. Parassidis ◽  
P. Tsekrekos
Keyword(s):  
Hilbert Space ◽  
Symmetric Operator ◽  
Sufficient Conditions ◽  
Positive Definite ◽  
Selfadjoint Extension ◽  
Symmetric Operators ◽  
Selfadjoint Extensions ◽  
Positive Extensions

LetA0be a closed, minimal symmetric operator from a Hilbert spaceℍintoℍwith domain not dense inℍ. LetA^also be a correct selfadjoint extension ofA0. The purpose of this paper is (1) to characterize, with the help ofA^, all the correct selfadjoint extensionsBofA0with domain equal toD(A^), (2) to give the solution of their corresponding problems, (3) to find sufficient conditions forBto be positive (definite) whenA^is positive (definite).

SUPERSYMMETRY IN 2+2 DIMENSIONS

2000 ◽  
Vol 15 (21) ◽  
pp. 1349-1355 ◽  
Author(s):  
F. T. BRANDT ◽  
D. G. C. MCKEON ◽  
T. N. SHERRY
Keyword(s):  
Hilbert Space ◽  
Positive Definite ◽  
The Other ◽  
Dirac Spinors

The analysis of spinors in 2+2 dimensions is reviewed. The two most basic supersymmetry algebras in this space are considered. In the simpler case, the supersymmetry generators are Majorana spinors. In the other algebra we consider, the supersymmetry generators are Dirac spinors. Consistency with having a positive definite Hilbert space is shown to be impossible in both of these particular cases.

scholarly journals Maximal nonnegative and $\theta$-accretive extensions of a positive definite linear relation

2020 ◽  
Vol 12 (2) ◽  
pp. 289-296
Author(s):  
O.G. Storozh
Keyword(s):  
Hilbert Space ◽  
Boundary Conditions ◽  
Linear Relation ◽  
Differential Operators ◽  
Boundary Value ◽  
Positive Definite ◽  
Complex Hilbert Space ◽  
Multivalued Operator ◽  
Linear Transformations ◽  
Accretive Extension

Let $L_{0}$ be a closed linear positive definite relation ("multivalued operator") in a complex Hilbert space. Using the methods of the extension theory of linear transformations in a Hilbert space, in the terms of so called boundary value spaces (boundary triplets), i.e. in the form that in the case of differential operators leads immediately to boundary conditions, the general forms of a maximal nonnegative, and of a proper maximal $\theta$-accretive extension of the initial relation $L_{0}$ are established.

A Parallel Dual Projection Algorithm for Solving Positive Definite Quadratic Programming Problems

1990 ◽  
Author(s):  
Yugang Wang ◽  
Eric Sandgren
Keyword(s):  
Quadratic Programming ◽  
Positive Definite ◽  
Parallel Computer ◽  
Projection Algorithm ◽  
Test Problems ◽  
Local Optimum ◽  
Active Constraint ◽  
Search Point ◽  
Quadratic Programming Problems ◽  
Dual Projection

Abstract Two parallel algorithms, a dual projection algorithm and a hybrid dual projection algorithm, are proposed for solving positive definite quadratic programming problems. In each iteration of the algorithms, the search point is always a local optimum point on the current active constraint basis for both adding and dropping constraint operations. The advantage of this strategy is that the computation is stable and all operations maintain parallel properties. Only a pseudo-inverse matrix must be updated instead of two matrices in Goldfarb’s dual algorithm in a basis change. Both computational and space complexities are reduced by about half. When the search point reaches a vertex in the dual space, a pivot operation is employed to update the basis in the hybrid dual projection algorithm in place of the addition and deletion operations in the dual projection algorithm. This reduces the computational complexity by half in future iterations. Some suggestions are presented to further enhance the computational speed of the algorithm. Numerical results are presented based on randomly generated test problems. Comparison with other methods demonstrates that the new algorithm is efficient and stable and points to the possibility of implementation on a parallel computer.

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