Linear programming techniques are useful in many diverse applications such as: production planning, energy distribution etc. To find an optimal solution of the linear programming problem, we have to repeat computations and it takes a lot of processing time. For high speed computation of linear programming, special purpose hardware has been sought. This paper proposes a systolic array for solving linear programming problems using the revised simplex method which is a typical algorithm of linear programming. This paper also proposes a modified systolic array that can solve linear programming problems whose sizes are very large.

In this paper, a new algorithm for selection of candidates for handwritten character recognition is presented. Since we adopt the concept of the marginal radius to examine the confidence of candidates, the evaluation function is required to describe the pattern distribution correctly. For this reason, we propose Simplified Mahalanobis distance and observe its behavior by simulation. In the proposed algorithm, first, for each character, two types of feature regions (multi-dimensional one and one-dimensional one) are estimated from training samples statistically. Then, by referring to the feature regions, candidates are selected and verified. Using two types of feature regions is a principal characteristic of our method. If parameters are estimated accurately, the multi-dimensional feature region is extremely effective for character recognition. But generally, estimation errors in parameters occur, especially with a small number of sample patterns. Although the recognition ability of one-dimensional feature region is not so high, it can express the distribution comparatively precisely in one-dimensional space. By combining these feature regions, they will work concurrently to overcome the defects of each other. The effectiveness of the method is shown with the results of experiments.