{"id":12760,"date":"2021-11-20T18:49:14","date_gmt":"2021-11-20T23:49:14","guid":{"rendered":"https:\/\/carleton.ca\/scs\/?page_id=12760"},"modified":"2021-11-20T18:49:14","modified_gmt":"2021-11-20T23:49:14","slug":"tr-95-02-stochastic-searching-on-the-line-and-its-applications-to-parameter-learning-in-nonlinear-optimization","status":"publish","type":"page","link":"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-1995\/tr-95-02-stochastic-searching-on-the-line-and-its-applications-to-parameter-learning-in-nonlinear-optimization\/","title":{"rendered":"TR-95-02: Stochastic Searching on the Line and its Applications to Parameter Learning in Nonlinear Optimization"},"content":{"rendered":"

Carleton University
\nTechnical Report<\/a> TR-95-02
\nJanuary 1995<\/p>\n

Stochastic Searching on the Line and its Applications to Parameter Learning in Nonlinear Optimization<\/h2>\n
\n
\n
B.J. Oommen<\/div>\n<\/div>\n<\/div>\n
\n

Abstract<\/h3>\n<\/div>\n

We consider the problem of a learning mechanism (for example, a robot) locating a point on a line when it is interacting with an random environment which essentially informs it, possibly erroneously, which way it should move. In this paper we present a novel scheme by which the point can be learnt using some recently devised learning principles. The heart of the strategy involves discretizing the space and performing a controlled random walk on this space. The scheme is shown to be e-optimal and to converge with probability 1. Although the problem is solved in its generality, its application in non-linear optimization has also been suggested. Typically, an optimization process involves working one’s way toward the maximum (minimum) using the local information that is available. However, the crucial issue in these strategies is that of determining the parameter to be used in the optimization itself. If the parameter is too small the convergence is sluggish. On the other hand, if the parameter is too large, the system could erroneously converge or even oscillate. Our strategy can be used to determine the best parameter to be used in the optimization.<\/p>\n

TR-95-02.pdf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Carleton University Technical Report TR-95-02 January 1995 Stochastic Searching on the Line and its Applications to Parameter Learning in Nonlinear Optimization B.J. Oommen Abstract We consider the problem of a learning mechanism (for example, a robot) locating a point on a line when it is interacting with an random environment which essentially informs it, possibly […]<\/p>\n","protected":false},"author":49,"featured_media":0,"parent":11736,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"yoast_head":"\nTR-95-02: Stochastic Searching on the Line and its Applications to Parameter Learning in Nonlinear Optimization - 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