New algorithm for polynomial spectral factorization with quadratic convergence. I
New algorithm for polynomial spectral factorization with quadratic convergence. II
New hybrid conjugate gradient method for nonlinear optimization with application to image restoration problems
The conjugate gradient method is one of the most effective algorithm for unconstrained nonlinear optimization problems. This is due to the fact that it does not need a lot of storage memory and its simple structure properties, which motivate us to propose a new hybrid conjugate gradient method through a convex combination of and . We compute the convex parameter using the Newton direction. Global convergence is established through the strong Wolfe conditions. Numerical experiments show the...
New iterative scheme for finite families of equilibrium, variational inequality, and fixed point problems in Banach spaces.
New quasi-Newton method for solving systems of nonlinear equations
We propose a new Broyden method for solving systems of nonlinear equations, which uses the first derivatives, but is more efficient than the Newton method (measured by the computational time) for larger dense systems. The new method updates QR or LU decompositions of nonsymmetric approximations of the Jacobian matrix, so it requires arithmetic operations per iteration in contrast with the Newton method, which requires operations per iteration. Computational experiments confirm the high efficiency...
Newton and conjugate gradient for harmonic maps from the disc into the sphere
We compute numerically the minimizers of the Dirichlet energyamong maps from the unit disc into the unit sphere that satisfy a boundary condition and a degree condition. We use a Sobolev gradient algorithm for the minimization and we prove that its continuous version preserves the degree. For the discretization of the problem we use continuous finite elements. We propose an original mesh-refining strategy needed to preserve the degree with the discrete version of the algorithm (which is a preconditioned...
Newton-Kantorovich method and its global convergence.
Newton-Krylov type algorithm for solving nonlinear least squares problems.
Newton's Method for Nonlinear Inequalities.
Newton's method in the context of gradients.
Newton's methods for variational inclusions under conditioned Fréchet derivative
Estimates of the radius of convergence of Newton's methods for variational inclusions in Banach spaces are investigated under a weak Lipschitz condition on the first Fréchet derivative. We establish the linear convergence of Newton's and of a variant of Newton methods using the concepts of pseudo-Lipschitz set-valued map and ω-conditioned Fréchet derivative or the center-Lipschitz condition introduced by the first author.
Nonlinear conjugate gradient methods
Modifications of nonlinear conjugate gradient method are described and tested.
Nonlinear conjugate gradient methods with sufficient descent condition for large-scale unconstrained optimization.
Nonlinear iterative methods and parallel computation
Nonlinear optimization using the generalized reduced gradient method
Nonlinear Rescaling Method and Self-concordant Functions
Nonlinear rescaling is a tool for solving large-scale nonlinear programming problems. The primal-dual nonlinear rescaling method was used to solve two quadratic programming problems with quadratic constraints. Based on the performance of primal-dual nonlinear rescaling method on testing problems, the conclusions about setting up the parameters are made. Next, the connection between nonlinear rescaling methods and self-concordant functions is discussed and modified logarithmic barrier function is...
Nonlinear Successive Over-Relaxation.
Nonlinear time-varying spectral analysis: HHT and MODWPT.
Nonmonotone strategy for minimization of quadratics with simple constraints
An algorithm for quadratic minimization with simple bounds is introduced, combining, as many well-known methods do, active set strategies and projection steps. The novelty is that here the criterion for acceptance of a projected trial point is weaker than the usual ones, which are based on monotone decrease of the objective function. It is proved that convergence follows as in the monotone case. Numerical experiments with bound-constrained quadratic problems from CUTE collection show that the modified...
Non-monotoneous parallel iteration for solving convex feasibility problems
The method of projections onto convex sets to find a point in the intersection of a finite number of closed convex sets in an Euclidean space, sometimes leads to slow convergence of the constructed sequence. Such slow convergence depends both on the choice of the starting point and on the monotoneous behaviour of the usual algorithms. As there is normally no indication of how to choose the starting point in order to avoid slow convergence, we present in this paper a non-monotoneous parallel algorithm...