|
|
 |
 |
|
6.856J / 18.416J 2002¬î©u½Òµ{¡GÀH¾÷ºtºâªk(Randomized Algorithms, Fall 2002)
|
|
¼f©w¡G«\©ºªZ(²¤¶¨Ã±H«H)
¼f©w²¤¶¡G ¤¤µØ¤j¾Ç¸ê¤u¨t°Æ±Ð±Â Ó¤H±Mªø¡Gºtºâªk(algorithms)¡BÂ÷´²¼Æ¾Ç(discrete mathematics)¡B¹Ï½×(graph theory)¤ÎµL½uºô¸ô(wireless networks) ¡C
½Ķ¡GÃC¹Å«T(²¤¶¨Ã±H«H)
½s¿è¡G¦¶¾ÇùÚ(²¤¶¨Ã±H«H)
|
|
 |
 |
 |
 |
 |
¤Á³Îºtºâªk¬O¥Î¨Ó¸Ñ¨M½ÆÂø¥B»Ý¤j¶qpºâªº°ÝÃD,¦p¥|¤¸¯ÀÁl±ªºµLµ²ºc®æºô©Ò¥Ü¡C(¼v¹³¤D¨ú§÷¦Û¬ü°ê¤ÓªÅÁ`¸p(NASA)ºô¯¸¡Ghttp://www.nasa.gov.) Partitioning algorithms are used to solve complex large scale computational problems, as shown in this unstructured grid for a four element airfoil. (Image is taken from NASA's Web site: http://www.nasa.gov.)
|
 |
|
½Òµ{«ÂI
³oÓ½Òµ{¾Ö¦³§¹¾ãªº±Â½ÒÁ¿¸q¥H¤Îªþ¤W¸Ñµªªº°ÝÃD
This course site features a full set of lecture notes and problem sets with solutions.
½Òµ{´yz
³o½Òµ{¬ã¨s¦p¦ó¥Î¶Ã¼Æ¨Ã³z¹LÀH·N©â¼Ë¡BÀH¾÷¿ï¾Üµýª«¡B¯}Ãa¹ïºÙ¥H¤Î°¨¥i¤ÒÃì¨Ï±oºtºâªk§ó²³æ©M§ó¦³®Ä²v¡C¥DÃD¥]¬A¡JÀH¾÷pºâ¡B¸ê®Æµ²ºc¡]Âø´êªí¡B¬Ù²¤¦ê¦C¡^¡B¹Ï½×ºtºâªk¡]³Ì¤pÂX±i¾ð¡A³Ìµu¸ô®|¡A³Ì¤Ö¤Á³Î¡^¡B´X¦óºtºâªk¡]¥Y´ß¡B¦b©T©w©Î¥ô·Nºû«×ªº½u©Ê³W¹º)¡Bªñ¦üp¼Æ¡B¥¦æºtºâªk¡B½u¤Wºtºâªk¡B®ø¥hÀH¾÷§Þ³N¡A¥H¤Îºtºâªkªº¾÷²v¤ÀªR¤u¨ã¡C
This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.
|
|
 |
| ®v¸ê |
|
Á¿®v¡G
David R. Karger ±Ð±Â
|
| ¤W½Ò®É¼Æ |
|
±Ð®v±Â½Ò¡G
¨C¶g2¸`
¨C¸`1.5¤p®É
|
| µ{«× |
|
¬ã¨s©Ò
|
| ¦^À³ |
| §i¶D§Ú̱z¹ï¥»½Òµ{©Î¡u¶}©ñ¦¡½Òµ{ºô¶¡vªº«ØÄ³¡C |
| Án©ú |
³Â¬Ù²z¤u¾Ç°|¶}©ñ¦¡½Òµ{»{¥i ¶}©ñ¦¡½Òµ{pµe¡]OOPS¡^ªºÂ½Ä¶pµe¡A¶}©ñ¦¡½Òµ{pµe¡]OOPS¡^¤D¬O¹B¥Î¨ä¿W¥ß¹Î¶¤¡B¿W¥ß¸ê·½¡B¿W¥ß¬yµ{¶i¦æÂ½Ä¶pµe¤§¹Î¶¤¡C
©Ò¦³³Â¬Ù²z¤u¾Ç°|¶}©ñ¦¡½Òµ{¤§§÷®Æ¬Ò¥H³Â¬Ù²z¤u¾Ç°|¶}©ñ¦¡½Òµ{³Ð§@¦@¨É±ÂÅvµo§G¡A©Ò¦³¤§Â½Ä¶¸ê®Æ¬Ò¥Ñ¶}©ñ¦¡½Òµ{pµe¡]OOPS¡^©Ò´£¨Ñ¡A¨Ã¥Ñ¨ät½Ķ«~½è¤§³d¥ô¡C
¦¹³B³Â¬Ù²z¤u¾Ç°|¶}©ñ¦¡½Òµ{¤§¸ê®Æ¤D¥Ñ ¶}©ñ¦¡½Òµ{pµe¡]OOPS¡^ ͬ°¥¿Å餤¤å¡C³Â¬Ù²z¤u¾Ç°|¶}©ñ¦¡½Òµ{¦b¦¹Án©ú¡A¤£½×¬O§_¾D¹J©Îµo²{¬ÛÃöijÃD¡A³Â¬Ù²z¤u¾Ç°|¶}©ñ¦¡½Òµ{¡B³Â¬Ù²z¤u¾Ç°|±Ð®v¡B³Â¬Ù²z¤u¾Ç°|®Õ¤è¨Ã¤£¹ï½Ķ¥¿½T«×¤Î§¹¾ã©Ê§@«OÃÒ¡C¤Wz³æ¦ì¨Ã¹ï½Ķ«á¤§¸ê®Æ¤£§@©ú¥Ü©ÎÀq³\¹ï¥ô¤@¯S©w¥Øªº¤§¾A¦X©Ê¤§«OÃÒ¡B«D«IÅv¤§«OÃÒ¡B©Î¥Ã¤£¥X¿ù¤§«OÃÒ¡C³Â¬Ù²z¤u¾Ç°|®Õ¤è¡B³Â¬Ù²z¤u¾Ç°|¶}©ñ¦¡½Òµ{¹ï½Ķ¤W¤§¤£¥¿½T¤£t¥ô¦ó³d¥ô¡C¥Ñ½Ķ©Ò¤Þµo¥ô¦óÃö©ó¦¹µ¥¸ê®Æ¤§¤£¥¿½T©Î¨ä¥L·å²«¡A¬Ò¥Ñ¶}©ñ¦¡½Òµ{pµe¡]OOPS¡^t¥þ³d¡A¦Ó«D³Â¬Ù²z¤u¾Ç°|¶}©ñ¦¡½Òµ{¤§³d¡C
ì¤åÁn©ú |
|
|
|
|
 |
 |
 |