In this paper, we study the iterative algorithms for saddle point problems(SPP). We present a new symmetric successive over-relaxation method with three parameters, which is the extension of the SSOR iteration method. Under some suitable conditions, we give the convergence results. Numerical examples further confirm the correctness of the theory and the effectiveness of the method. Key–Words: i...

We propose a class of regularized Hermitian and skew-Hermitian splitting methods for the solution of large, sparse linear systems in saddle-point form. These methods can be used as stationary iterative solvers or as preconditioners for Krylov subspacemethods.We establish unconditional convergence of the stationary iterations and we examine the spectral properties of the corresponding preconditi...

Consider linear systems whose matrix and right-hand side vector depend affinelinearly on parameters varying within prescribed intervals. We present a C-XSC [9] implementation of a parametric fixed-point iteration method for the verified enclosure of the parametric solution set. Input data for the system have an entirely numerical representation by dense twodimensional matrices. Some specific fe...

Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent’s belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate...

In this article, a new multidirectional monotone hybrid iteration algorithm for finding a solution to the split common fixed point problem is presented for two countable families of quasi-nonexpansive mappings in Banach spaces. Strong convergence theorems are proved. The application of the result is to consider the split common null point problem of maximal monotone operators in Banach spaces. ...

We develop a point based method for solving finitely nested interactive POMDPs approximately. Analogously to point based value iteration (PBVI) in POMDPs, we maintain a set of belief points and form value functions composed of those value vectors that are optimal at these points. However, as we focus on multiagent settings, the beliefs are nested and computation of the value vectors relies on p...

We study a principal-agent game where the principal commits to an affine contract. We suppose that the principal has incomplete information but he can adjust the contract according to the myopically behaving agent’s reactions when the game is played repeatedly. The adjustment process can be considered as a learning model. We derive convergence conditions for fixed-point iteration as an adjustme...

The study of random fixed points has been an active area of contemporary research in mathematics. Some of the recent works in this field are noted in [1, 2, 3, 11]. In particular, random iteration schemes leading to random fixed points were introduced in [3]. After that, random iterations for finding solutions of random operator equations and fixed points of random operators have been discussed...

We convert, via a version that uses constraints, a type inference system for strictness analysis into an algorithm which given an expression finds the set of possible typings. Although this set in general does not possess a minimal element, it can be represented compactly by means of symbolic expressions in normal form – such expressions have the property that once values for the constraint var...