The multi-directional (MD) technique is a general purpose tool for optimisation, that is, finding the global maxima or minima of some objective function in a given domain. Any function that produces a relatively continuous surface may therefore be suitable. Using a graphics processing unit (GPU) for MD optimisation demonstrates an increase in speed of up to 400-fold compared to using a central processing unit (CPU). More than a 100-fold speed up was seen across a range of problems. This was achieved despite non-trivial amounts of branching in the algorithm. The main problem investigated was a particular form of portfolio optimisation. As a further test, the same algorithm was used to find the minima of the Schwefel function, which has several local minima and is a particularly difficult candidate for numerical optimisation. The great exibility of the MD technique and the performance obtained strongly suggests that the GPU implementation has great potential in finance.<\/p>\n","protected":false},"excerpt":{"rendered":"
The multi-directional (MD) technique is a general purpose tool for optimisation, that is, finding the global maxima or minima of some objective function in a given domain. Any function that produces a relatively continuous surface may therefore be suitable. Using a graphics processing unit (GPU) for MD optimisation demonstrates an increase in speed of up […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,89,576,3],"tags":[1787,14,1804,20,199],"class_list":["post-6476","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-finance","category-paper","tag-algorithms","tag-cuda","tag-finance","tag-nvidia","tag-tesla-c1060"],"views":1951,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6476","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6476"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6476\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6476"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6476"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6476"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}