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Dodgers–Yankees rivalry
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Dodgers–Yankees rivalry
Los Angeles Dodgers
New York Yankees
First meeting
October 1, 1941
Yankee Stadium (original) (World Series)
June 18, 2004
Dodger Stadium (regular season)
Latest meeting
September 14, 2016
Dodger Stadium
Dodgers 2, Yankees 0
Next meeting
TBD
Statistics
Meetings total
78 (World Series: 65, regular season: 13)
Regular season series
Dodgers, 7–6
Largest victory
12–2, Yankees (World Series–October 15, 1978)
6–0, Dodgers (regular season–June 19, 2013)
Longest win streak
Dodgers: 2 (June 19, 2013–July 30, 2013)
Yankees: 2 (June 27, 2010–June 19, 2013)
Current win streak
1, Dodgers
Post-season history
1941 World Series: Yankees defeat Dodgers, 4–1
1947 World Series: Yankees defeat Dodgers, 4–3
1949 World Series: Yankees defeat Dodgers, 4–1
1952 World Series: Yankees defeat Dodgers, 4–3
1953 World Series: Yankees defeat Dodgers, 4–1
1955 World Series: Dodgers defeat Yankees, 4–3
1956 World Series: Yankees defeat Dodgers, 4–3
1963 World Series: Dodgers defeat Yankees, 4–0
1977 World Series: Yankees defeat Dodgers, 4–2
1978 World Series: Yankees defeat Dodgers, 4–2
1981 World Series: Dodgers defeat Yankees, 4–2
The Dodgers–Yankees rivalry is a Major League Baseball (MLB) rivalry between the Los Angeles Dodgers and the New York Yankees. The Dodgers are a member club of the National League (NL) West division, and the Yankees are a member club of the American League (AL) East division. The rivalry between the Dodgers and Yankees is one of the most well-known rivalries in Major League Baseball.[1] The two teams have met 11 times in the World Series, more times than any other pair of teams from the American and National Leagues.[1] The initial significance was embodied in the two teams' proximity in New York City, when the Dodgers initially played in Brooklyn. After the Dodgers moved to Los Angeles in 1958, the rivalry retained its significance as the two teams represented the dominant cities on each coast of the United States, and since the 1980s, the two largest cities in the United States. The Dodgers currently lead the regular season series 7-6. Although the rivalry's significance arose from the two teams' numerous World Series meetings,[1] the Yankees and Dodgers have not met in the World Series since 1981.[1] They would not play each other in a non-exhibition game until 2004, when they played a 3-game interleague series.[2] Nevertheless, games between the two teams have become quite popular and draw sellout crowds.[3]
Contents
[hide]
1 While in New York
1.1 A new team in town, new stadiums built, Subway Series established
1.2 1941: First meeting between the Bronx Bombers and "Dem Bums"
1.3 1947-53: "Wait 'til next year!"
1.4 1955: "This IS next year!"
1.5 1956: A "perfect" rematch
2 Dodgers move to Los Angeles
2.1 1963: Los Angeles Dodger win
3 World Series appearances in the 1970s and 1981
3.1 1977: The Bronx is Burning
3.2 1978: Bucky Dent's Playoffs
3.3 1981: Changing of the Guard
4 Rivalry in the 21st Century
4.1 First Interleague matchup
4.2 The Yankee Years controversy
4.3 Back to the Bronx
5 See also
5.1 Histories
6 References
While in New York[edit]
A new team in town, new stadiums built, Subway Series established[edit]
Main articles: Subway Series and Giants–Yankees rivalry
At the dawn of the 20th century, the American League was in its infancy, and one of its charter franchises (which would eventually become the Yankees) was the original Baltimore Orioles. The league, however, recognized that it required a presence in New York City, the country's largest market, in order to survive. Hence, it moved the Orioles to New York, which was already the home of two National League franchises, the Brooklyn Dodgers and New York Giants. The new team was eventually called the New York Highlanders due to their home field being Hilltop Park from 1903–1912.
After the Highlanders allowed the Giants to play at Hilltop Park when the latter's home stadium, the Polo Grounds, was undergoing reconstruction from a fire, the Giants invited the Highlanders to share the Polo Grounds with them. Since the Highlanders were no longer playing at higher elevations, they changed their name to the New York Yankees. The Dodgers also opened up their new field, Ebbets Field, in Brooklyn. Their opening game at the stadium was an exhibition game against the Highlanders on April 5, 1913 where future Yankee manager Casey Stengel had the game-winning hit for the Dodgers. The Highlanders would officially be called the Yankees later that year.
In 1920, the Giants notified the Yankees that they would have to find a new stadium of their own for the 1921 season.[4] The Giants rescinded that eviction notice and allowed the Yankees to stay until the end of the 1922 season, when the Giants renovated the Polo Grounds and increased seating capacity from 38,000 to 50,000.[4] Before that however, the 1921 World Series between the Giants and Yankees became the first Subway Series, as match-ups between the Dodgers and the Giants were not referred to as Subway Series. After losing to the Giants in that World Series and again the following year, the Yankees moved across the Harlem River to the original Yankee Stadium in the Bronx, where they captured their first ever World Series from the Giants in 1923.
1941: First meeting between the Bronx Bombers and "Dem Bums"[edit]
Main article: 1941 World Series
The rivalry between the Yankees and Brooklyn Dodgers began when the teams first met in the 1941 World Series. In Game 4, with the Yankees leading the series two games to one, the Dodgers led by one going to the top of the ninth inning and, with two outs, just barely missed getting the third out and wound up losing the game. With two outs, the Yankees' Tommy Henrich swung and missed with two strikes, but reached on a passed ball, as Dodger catcher Mickey Owen failed to hold on to the pitch. Henrich reached first base on the play. Owen recalled the incident:
It was a great breaking curve that I should have had, but I guess the ball hit the side of my glove. It got away from me, and by the time I got hold of it, near the corner of the Brooklyn dugout, I couldn't have thrown anybody out at first.[5]
Joe DiMaggio followed Henrich's at bat with a single before Charlie Keller hit a double to drive in both Henrich and DiMaggio and give the Yankees a 5-4 lead. After Bill Dickey walked, he and Keller scored on a Joe Gordon double to make the final score 7-4. The next day, the Yankees clinched the first series match-up between the two teams in the start of what would become a long-lasting rivalry.
1947-53: "Wait 'til next year!"[edit]
Main articles: 1947 World Series, 1949 World Series, 1952 World Series, and 1953 World Series
Six years later, the 1947 World Series had a dramatic moment in Game 6. The Dodgers' Al Gionfriddo was placed in left field for defensive purposes and robbed Joe DiMaggio of a game-tying three-run home run. DiMaggio was visibly disgusted by the outcome of the play when rounding the bases in one of the few emotional displays of his career. The Dodgers won the game and forced a do-or-die Game 7, only to fall.
Dazzling defensive plays would be seen again, this time by the Yankees in the 1952 contest, as Billy Martin came outside the camera coverage area to catch a pop-fly after Brooklyn had threatened to take the lead in Game 7. Martin would go on to be the hero once more in the 1953 series with a series record 12 hits, including the winning hit of the series-clinching game off of Clem Labine.
Brooklyn signed Jackie Robinson to not only break the color line, but to bolster the lineup. Robinson, along with outfielder Duke Snider and pitcher Don Newcombe, sparked Brooklyn to four National League pennants between 1947 and 1953. Each and every time during this period, however, the World Series ended in heartbreak for the Dodgers and gave the Dodger fans their rallying cry: "Wait 'til next year!"[6]
During the 1953 World Series, long time Dodgers announcer Red Barber refused to man the broadcast booth due to a compensation dispute with Gillette. Barber jumped ship to the Yankees and joined Mel Allen to call the games for the Yankees. Struggling to find a replacement, the Dodgers gave the call to a 25-year-old named Vin Scully, the youngest man to ever call a World Series game for a major network. Scully would go on to continue as a Dodgers broadcaster for another 6 decades.
1955: "This IS next year!"[edit]
Main article: 1955 World Series
Finally, in 1955 the Dodgers reversed matters, prevailing over the Yankees in seven games to win their only World Series in Brooklyn.[7] Thus came the slogan, This IS next year.
1956: A "perfect" rematch[edit]
Main articles: 1956 World Series and Don Larsen's perfect game
Brooklyn fell short of repeating the next season, falling in seven games to the Yankees. That year's team suffered some ignominy in being on the losing end of Don Larsen's perfect game in Game 5, which was the first of only two no-hitters ever pitched in postseason play and the only in the history of the World Series.
In both the 1955 and 1956 World Series, the home team won the first six games of the World Series, but lost Game 7. It would not be until 1987 when the home team won all seven games of a World Series.[8] However, the Yankees would meet the unfortunate fate of being the first American League team to lose a World Series in which the home team won all seven games, in 2001.[8][9]
The 1956 World Series would be the last Subway Series of the 1900s and the last to include one of the National League's charter franchises.
Dodgers move to Los Angeles[edit]
1963: Los Angeles Dodger win[edit]
After the Dodgers' move to Los Angeles for the 1958 season, it would take them two dominating pitchers (Sandy Koufax and Don Drysdale), a speedy shortstop (Maury Wills) and a great outfielder (Tommy Davis) to spark them to a pennant in 1963. They swept the aging Yankees (consisting of Mickey Mantle, Roger Maris, and Whitey Ford) in four straight games to win their second World Series since moving to Los Angeles, having won in 1959, with the Bronx Bombers not taking a single lead against the powerful Los Angeles pitching staff and being limited to just four runs in the entire series,[10] the very first meeting between teams from New York City and Los Angeles for a major professional sports championship.[10][11]
Dodgers broadcaster Vin Scully said that although the Dodgers won four World Series titles in 10 years, he said that this championship was the biggest of those four because "the ultimate was not only beating the Yankees but sweeping them in four", but said that "to New York fans it was still the old Brooklyn Dodgers and there was a lot of bitterness toward them."[12]
World Series appearances in the 1970s and 1981[edit]
1977: The Bronx is Burning[edit]
Mr. October: Reggie Jackson hit three home runs in Game 6 of the 1977 World Series against the Dodgers to win the series for the Yankees.
After 14 years, sophomore manager Tommy Lasorda led a young Dodgers team to the 1977 World Series where they faced Billy Martin and the Yankees. The two managers were involved in a fist fight during the 1956 season while playing for the two teams they were now managing.[13] The Yankees were coming off a controversy ridden season. Furious at their loss to The Big Red Machine in the 1976 World Series, owner George Steinbrenner had signed slugger Reggie Jackson to the team. Jackson immediately created friction in the clubhouse between himself and Bill Martin as well as captain Thurman Munson, the defending AL MVP. New York City itself was going through a financial crisis under Mayor Abraham Beame and experienced the Son of Sam and a major blackout, all chronicled in The Bronx is Burning. Despite the controversy, the Yankees managed to play together and win the pennant to face the Dodgers in the World Series.
The Dodgers featured an infield of Steve Garvey at first, Davey Lopes at second, Ron Cey at third, and Bill Russell at shortstop, in addition to slugger Reggie Smith, and pitching duo (Don Sutton and Tommy John). In addition to Jackson and Munson, the Yankees had Bucky Dent and Graig Nettles, Cy Young Award-winning closer Sparky Lyle, young pitcher Ron Guidry, and speedsters Willie Randolph and Mickey Rivers. The Dodgers appeared primed to win the Series, but Reggie Jackson put on his "Mr. October" show as he hit three home runs in Game 6 off of 3 pitches to lead the Yanks to their first World Series championship since 1962.
1978: Bucky Dent's Playoffs[edit]
The next season, the Yankees won their division in thrilling fashion, thanks in large part to a timely home run from Bucky Dent in a one-game playoff against the Boston Red Sox. They went to the World Series for the third straight year where they faced the Dodgers for the second straight year. The Dodgers won the first two games of the Series thanks to rookie pitcher Bob Welch, but New York won the next four to take the 75th Fall Classic.
1981: Changing of the Guard[edit]
In 1981, the fortunes turned in LA's favor, as rookie pitcher Fernando Valenzuela won National League Rookie of the Year and Cy Young Award honors. But the Yankees had American League Rookie of the Year Dave Righetti (who was traded from the Rangers in exchange for Lyle) and daunting 1-2 relief punch of set-up man Ron Davis and closer Rich Gossage. The Yankees won the first two contests, but LA, led by Valenzuela and first baseman Steve Garvey, won the next four to claim their first World Series title since 1965 in a strike-shortened season.[10][14] Fallout from the series would eventually have Reggie Jackson be controversially dismissed a bit later and owner George Steinbrenner to start a public feud with Dave Winfield, who had a dismal performance in the series.[15] After the series ended, Steinbrenner issued a public apology to the City of New York for his team's performance, while at the same time assuring the fans that plans to put the team together for 1982 would begin immediately.[16][17] The Yankee owner was criticized heartily by players and press alike for doing so, as most people felt losing in the World Series was not something that needed to be apologized for.[7]
Lasorda said of the revenge for what Jackson did to the Dodgers in 1977 and 1978: "We were suffering and the guy was making a fool out of us. I was hoping and praying we would get another shot at him."[12]
This led to the Yankees not winning a World Series in the 1980s, the only decade they would not have won a championship since moving to the Bronx.[15][18][19] In contrast, the Dodgers were able to defeat the Oakland Athletics in the 1988 World Series.[20][21] It would be their last post-season meeting to date, the last time ever the Dodgers visited the old Yankee Stadium, and the last meeting between teams from New York City and Los Angeles for a major professional sports championship until the Rangers and the Kings squared off in the 2014 Stanley Cup Finals.[10][12]
Rivalry in the 21st Century[edit]
Yankees great Don Mattingly, then manager of the Dodgers.
Although the Yankees and the Dodgers haven't met in the playoffs since the 1981 World Series, interleague matchups between the two teams, whether in New York or Los Angeles, have become quite popular. Tickets in both cities have become exclusive shortly after going on sale and games between the two teams have drawn sell out crowds, as evidenced during the doubleheader at the new Yankee Stadium in June 2013, the first time the Dodgers visited the Bronx since interleague play was introduced and the first time they visited the new Yankee Stadium.[3]
First Interleague matchup[edit]
After 23 years of not facing the Yankees, LA hosted an interleague weekend series in June 2004 where they took two out of three contests. One ending had closer Éric Gagné strike out Bernie Williams for the final out. The third and final game was the ESPN Sunday Night game that weekend.
The rivalry was renewed when Joe Torre, who led the Yankees to four World Series championships, accepted a three-year, $13 million contract to manage the Dodgers on November 1, 2007.[22] He also brought along former Yankee first baseman Don Mattingly and Larry Bowa to his coaching staff. Coaches Rick Honeycutt and Mariano Duncan each played for both teams. Torre and left fielder Manny Ramirez, who had been a part of the fierce Yankees–Red Sox rivalry before being traded to the Dodgers mid-season, led the Dodgers to the 2008 West Division Pennant. The Yankees failed to make the 2008 Postseason, ending their 13-year consecutive postseason appearance streak with 12 of which were under Torre's.
The Yankee Years controversy[edit]
In 2009, Torre co-authored a book, The Yankee Years, about his time in New York that criticized principal owner George Steinbrenner and third baseman Alex Rodriguez.[1] The book cited that the term "A-Fraud" was used as a name for Alex Rodriguez's common nickname "A-Rod." The book specifically stated on p 245:
Back in 2004, at first Rodriguez did his best to try to fit into the Yankee culture – his cloying, B-grade actor best. He slathered on the polish. People in the clubhouse, including teammates and support personnel, were calling him ‘A-Fraud’ behind his back.
Torre would later explain the comments as being jestful and in good humor by Torre and Larry Bowa with Rodriguez.[23]
Both teams made it to the playoffs that year, but the Dodgers lost in the NLCS for the second straight year to the defending champion Philadelphia Phillies (whom the Dodgers beat in the NLCS in 1977 and 1978 before losing to the Yankees in the World Series in both years), who would lose to the Yankees in the World Series, giving New York their first championship since Torre left as manager.
The Yankees and Dodgers met again at Dodger Stadium for an interleague series from June 25–27, 2010.[1] Joe Torre and Don Mattingly played against the Yankees, who won the series two games to one, for the first time since joining the Dodgers and Torre's first meeting with Rodriguez since the writing of Torre's book.[24][25]
Back to the Bronx[edit]
Mattingly succeeded Torre as manager of the Dodgers after the 2010 season.[26][27] The Dodgers never played a regular season series at the original Yankee Stadium. The teams finally met up during the 2013 season.[28] The double header of June 19 was not just the Dodgers return to the Bronx after a long absence; it was also Mattingly's first return to the Bronx since he became Dodger manager (excluding Mattingly's visit to new Yankee Stadium on the night they honored George Steinbrenner with a monument in Monument Park).[29] Former Dodger pitcher Hiroki Kuroda won the first game for the Yankees while the Dodgers rebounded in the night cap behind the pitching of Chris Capuano. The two then split a 2-game series at Dodger stadium. Their most recent meeting occurred from September 12-14, 2016 at Yankee Stadium, where the Dodgers took two out of three games from the Yankees.
See also[edit]
Histories[edit]
Brooklyn Dodgers
Los Angeles Dodgers
New York Yankees
References[edit]
^ Jump up to: a b c d e f Nightengale, Bob (June 25, 2010). "Oscars of interleague: Stars coming out for Yankees-Dodgers". USA Today. p. 4C.
Jump up ^ Antonen, Mel (June 18, 2004). "Dodgers play host to Yankees in classic old-rivalry series". USA Today. p. 1C.
^ Jump up to: a b "New York Vs. LA Can Be As Big As It Gets: 'It's Great For Hockey'". CBSLosAngeles.com. June 3, 2014. Retrieved May 15, 2015. Interleague match-ups between the Dodgers and Yankees, in both markets, have proved to be largely popular, with tickets in Los Angeles becoming exclusive shortly after going on sale. A double-header in June 2013 in the Bronx also attracted sell out crowds at the new Yankees Stadium.
^ Jump up to: a b http://sabr.org/bioproj/park/58d80eca
Jump up ^ Schwartz, Larry (November 19, 2003). "Great Subway Series moments". ESPN Classic. ESPN Internet Ventures.
Jump up ^ Swan, Lisa. "Who's a Bum? For Once, the Yanks". New York: www.nydailynews.com. Archived from the original on September 6, 2009. Retrieved 2 August 2010.
^ Jump up to: a b Anderson, Dave (October 26, 2003). "Yanks Are Now 0-4 on the Brink at the Stadium". New York Times. p. 8.4.
^ Jump up to: a b Walker, Ben (November 5, 2001). "Diamondbacks 3, Yankees 2". Associated Press. The other two all-home victory Series were both won by Minnesota, 1987 and 1991.
Jump up ^ Olney, Buster (November 5, 2001). "In Final Twist, New York Falls in Ninth". New York Times. p. A1.
^ Jump up to: a b c d Barnes, Mike (June 1, 2014). "Stanley Cup Final: Kings vs. Rangers in L.A.-New York Championship Duel". The Hollywood Reporter. Retrieved May 15, 2015.
Jump up ^ Branch, John (June 5, 2014). "New York vs. Los Angeles: Rivalry Revived". The New York Times. p. B11.
^ Jump up to: a b c Dahlberg, Tim (June 3, 2014). "New York vs. LA a long time in the making, especially in hockey". FoxNews.com. Associated Press. Retrieved May 15, 2015.
Jump up ^ "Video". CNN. September 30, 1985. Archived from the original on September 25, 2008.
Jump up ^ Chass, Murray (October 29, 1981). "Dodgers Beat Yanks, 9-2, To Win the World Series". New York Times. p. A1.
^ Jump up to: a b Curry, Jack (August 7, 1994). "Flashback to '81: Another Lead, Another Strike". The New York Times. p. A1.
Jump up ^ "Steinbrenner Makes an Apology to the Fans for How Yankees Played". Los Angeles Times. Associated Press. October 29, 1981. p. G1.
Jump up ^ Gross, Jane (October 29, 1981). "Steinbrenner Issues an Apology to Fans". New York Times. p. B13.
Jump up ^ Costello, Brian (August 8, 2004). "'94 Yanks Cut Short". New York Post. p. 58.
Jump up ^ Amore, Dom (May 15, 2005). "Buck". The Hartford Courant. p. E8.
Jump up ^ "The Impossible Dream: Dodgers World Champs!". Los Angeles Times. October 21, 1988. p. 1.
Jump up ^ McManis, Sam (October 21, 1988). "It's a Title Out of the Blue". Los Angeles Times. p. 1.
Jump up ^ "Torre succeeds Little as Dodgers manager". ESPN.com. Associated Press. November 1, 2007.
Jump up ^ Kepner, Tyler (February 4, 2009). "Torre Tries to Spin 'A-Fraud' Controversy". The New York Times.
Jump up ^ Shpigel, Ben (June 28, 2010). "The Yankees Rally to Tie and Homer to Win". New York Times. p. D1.
Jump up ^ Costa, Brian (June 28, 2010). "Mattingly Refuses to Play the What-If Game". Wall Street Journal. p. A30.
Jump up ^ Gurnick, Ken (September 17, 2010). "Mattingly to replace Torre as Dodgers' skipper". MLB.com. dodgers.com.
Jump up ^ Hernandez, Dylan (September 18, 2010). "Dodgers: Changing of the Guard; Torre Out, Mattingly in As Dodgers Manager". Los Angeles Times. p. C1.
Jump up ^ "Dodgers Schedule (June 2013)". Mlb.com. Major League Baseball. Retrieved 13 September 2012.
Jump up ^ "Joe Torre, Don Mattingly Among Those To Honor George Steinbrenner Tonight". bleacherreport.com. Bleacher Report. Retrieved 12 October 2013.
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Jimmy Bryant (singer)
From Wikipedia, the free encyclopedia
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For the guitarist, see Jimmy Bryant.
James Howard Bryant (born June 2, 1929) is a singer, arranger and composer. He is most well known for providing the singing voice of Tony (played onscreen by Richard Beymer) in the 1961 film musical West Side Story.[1] While he received no screen credit, he states that Beymer was "a nice guy, and every time he did an interview he would mention my name."[1] He also sang for James Fox in the 1967 film musical Thoroughly Modern Millie,[1] and sang in "The Telephone Hour" number in Bye Bye Birdie. He also sang in the group that performed the theme song of the TV series Batman.[1]
Bryant was born in Birmingham, Alabama and grew up in Tarrant, Alabama. He attended Birmingham Southern College and the Birmingham Conservatory of Music before receiving a Scholarship in Composition at the New England Conservatory of Music in Boston, Massachusetts. He then moved to New York City in 1953 where he worked as a background singer.[1] He later moved to Los Angeles and played bass[1] in the house band at Puccini's, a Beverly Hills restaurant owned by Frank Sinatra.[1]
His work as an orchestrator includes the movies Not with My Wife, You Don't!, Penelope and the TV series Lost in Space.[1] He also composed music heard at Walt Disney World and Tokyo Disneyland.[1]
Bryant also scored the music for numerous radio and television commercials for clients such as Lone Star beer, Tecate, Chrysler Imports, and Toyota Motor Cars.
He was a friend of Jimmy Bryant, the famous guitar player, with whom he is often confused.
References[edit]
^ Jump up to: a b c d e f g h i "Jimmy Bryant". Alabama Music Hall of Fame. Archived from the original on 2008-12-07. Retrieved 2008-04-11. Citing Carlton, Bob (2002-07-12). "State native was 'ghost singer' in 'West Side Story'". Birmingham News. |access-date= requires |url= (help)
External links[edit]
Jimmy Bryant on IMDb
Authority control
WorldCat Identities
VIAF: 120741274
ISNI: 0000 0000 7950 4981
MusicBrainz: 4a7c5179-fb03-4bdb-a730-d851af753762

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Retrieved from "https://en.wikipedia.org/w/index.php?title=Jimmy_Bryant_(singer)&oldid=798687861"
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A* search algorithm
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In computer science, A* (pronounced as "A star") is a computer algorithm that is widely used in pathfinding and graph traversal, which is the process of plotting an efficiently directed path between multiple points, called "nodes". It enjoys widespread use due to its performance and accuracy. However, in practical travel-routing systems, it is generally outperformed by algorithms which can pre-process the graph to attain better performance,[1] although other work has found A* to be superior to other approaches.[2]
Peter Hart, Nils Nilsson and Bertram Raphael of Stanford Research Institute (now SRI International) first published the algorithm in 1968.[3] It is an extension of Edsger Dijkstra's 1959 algorithm. A* achieves better performance by using heuristics to guide its search.
Contents
[hide]
1 History
2 Description
2.1 Pseudocode
2.2 Example
3 Properties
3.1 Special cases
3.2 Implementation details
4 Admissibility and optimality
4.1 Bounded relaxation
5 Complexity
6 Applications
7 Relations to other algorithms
8 Variants
9 See also
10 Notes
11 References
12 Further reading
13 External links
History[edit]
A* was invented by researchers working on Shakey the Robot's path planning.
In 1968, AI researcher Nils Nilsson was trying to improve the path planning done by Shakey the Robot, a prototype robot that could navigate through a room containing obstacles. This path-finding algorithm, which Nilsson called A1, was a faster version of the then best known method, Dijkstra's algorithm, for finding shortest paths in graphs. Bertram Raphael suggested some significant improvements upon this algorithm, calling the revised version A2. Then Peter E. Hart introduced an argument that established A2, with only minor changes, to be the best possible algorithm for finding shortest paths. Hart, Nilsson and Raphael then jointly developed a proof that the revised A2 algorithm was optimal for finding shortest paths under certain well-defined conditions.
Description[edit]
A* is an informed search algorithm, or a best-first search, meaning that it solves problems by searching among all possible paths to the solution (goal) for the one that incurs the smallest cost (least distance travelled, shortest time, etc.), and among these paths it first considers the ones that appear to lead most quickly to the solution. It is formulated in terms of weighted graphs: starting from a specific node of a graph, it constructs a tree of paths starting from that node, expanding paths one step at a time, until one of its paths ends at the predetermined goal node.
At each iteration of its main loop, A* needs to determine which of its partial paths to expand into one or more longer paths. It does so based on an estimate of the cost (total weight) still to go to the goal node. Specifically, A* selects the path that minimizes
f ( n ) = g ( n ) + h ( n ) {\displaystyle f(n)=g(n)+h(n)}
where n is the last node on the path, g(n) is the cost of the path from the start node to n, and h(n) is a heuristic that estimates the cost of the cheapest path from n to the goal. The heuristic is problem-specific. For the algorithm to find the actual shortest path, the heuristic function must be admissible, meaning that it never overestimates the actual cost to get to the nearest goal node.
Typical implementations of A* use a priority queue to perform the repeated selection of minimum (estimated) cost nodes to expand. This priority queue is known as the open set or fringe. At each step of the algorithm, the node with the lowest f(x) value is removed from the queue, the f and g values of its neighbors are updated accordingly, and these neighbors are added to the queue. The algorithm continues until a goal node has a lower f value than any node in the queue (or until the queue is empty).[a] The f value of the goal is then the length of the shortest path, since h at the goal is zero in an admissible heuristic.
The algorithm described so far gives us only the length of the shortest path. To find the actual sequence of steps, the algorithm can be easily revised so that each node on the path keeps track of its predecessor. After this algorithm is run, the ending node will point to its predecessor, and so on, until some node's predecessor is the start node.
As an example, when searching for the shortest route on a map, h(x) might represent the straight-line distance to the goal, since that is physically the smallest possible distance between any two points.
If the heuristic h satisfies the additional condition h(x) ≤ d(x, y) + h(y) for every edge (x, y) of the graph (where d denotes the length of that edge), then h is called monotone, or consistent. In such a case, A* can be implemented more efficiently—roughly speaking, no node needs to be processed more than once (see closed set below)—and A* is equivalent to running Dijkstra's algorithm with the reduced cost d'(x, y) = d(x, y) + h(y) − h(x).
Pseudocode[edit]
The following pseudocode describes the algorithm:
function A*(start, goal) // The set of nodes already evaluated closedSet := {} // The set of currently discovered nodes that are not evaluated yet. // Initially, only the start node is known. openSet := {start} // For each node, which node it can most efficiently be reached from. // If a node can be reached from many nodes, cameFrom will eventually contain the // most efficient previous step. cameFrom := an empty map // For each node, the cost of getting from the start node to that node. gScore := map with default value of Infinity // The cost of going from start to start is zero. gScore[start] := 0 // For each node, the total cost of getting from the start node to the goal // by passing by that node. That value is partly known, partly heuristic. fScore := map with default value of Infinity // For the first node, that value is completely heuristic. fScore[start] := heuristic_cost_estimate(start, goal) while openSet is not empty current := the node in openSet having the lowest fScore[] value if current = goal return reconstruct_path(cameFrom, current) openSet.Remove(current) closedSet.Add(current) for each neighbor of current if neighbor in closedSet continue	// Ignore the neighbor which is already evaluated. if neighbor not in openSet	// Discover a new node openSet.Add(neighbor) // The distance from start to a neighbor //the "dist_between" function may vary as per the solution requirements. tentative_gScore := gScore[current] + dist_between(current, neighbor) if tentative_gScore >= gScore[neighbor] continue	// This is not a better path. // This path is the best until now. Record it! cameFrom[neighbor] := current gScore[neighbor] := tentative_gScore fScore[neighbor] := gScore[neighbor] + heuristic_cost_estimate(neighbor, goal) return failure function reconstruct_path(cameFrom, current) total_path := {current} while current in cameFrom.Keys: current := cameFrom[current] total_path.append(current) return total_path
Remark: the above pseudocode assumes that the heuristic function is monotonic (or consistent, see below), which is a frequent case in many practical problems, such as the Shortest Distance Path in road networks. However, if the assumption is not true, nodes in the closed set may be rediscovered and their cost improved. In other words, the closed set can be omitted (yielding a tree search algorithm) if a solution is guaranteed to exist, or if the algorithm is adapted so that new nodes are added to the open set only if they have a lower f value than at any previous iteration.
Illustration of A* search for finding path from a start node to a goal node in a robot motion planning problem. The empty circles represent the nodes in the open set, i.e., those that remain to be explored, and the filled ones are in the closed set. Color on each closed node indicates the distance from the start: the greener, the farther. One can first see the A* moving in a straight line in the direction of the goal, then when hitting the obstacle, it explores alternative routes through the nodes from the open set.
See also: Dijkstra's algorithm
Example[edit]
An example of an A* algorithm in action where nodes are cities connected with roads and h(x) is the straight-line distance to target point:
Key: green: start; blue: goal; orange: visited
The A* algorithm also has real-world applications. In this example, edges are railroads and h(x) is the great-circle distance (the shortest possible distance on a sphere) to the target. The algorithm is searching for a path between Washington, D.C. and Los Angeles.
Properties[edit]
Like breadth-first search, A* is complete and will always find a solution if one exists provided d ( x , y ) > ε > 0 {\textstyle d(x,y)>\varepsilon >0} for fixed ε {\displaystyle \varepsilon } .
If the heuristic function h is admissible, meaning that it never overestimates the actual minimal cost of reaching the goal, then A* is itself admissible (or optimal) if we do not use a closed set. If a closed set is used, then h must also be monotonic (or consistent) for A* to be optimal. This means that for any pair of adjacent nodes x and y, where d ( x , y ) {\displaystyle d(x,y)} denotes the length of the edge between them, we must have:
h ( x ) ≤ d ( x , y ) + h ( y ) {\displaystyle h(x)\leq d(x,y)+h(y)}
This ensures that for any path X from the initial node to x:
L ( X ) + h ( x ) ≤ L ( X ) + d ( x , y ) + h ( y ) = L ( Y ) + h ( y ) {\displaystyle L(X)+h(x)\leq L(X)+d(x,y)+h(y)=L(Y)+h(y)}
where L is a function that denotes the length of a path, and Y is the path X extended to include y. In other words, it is impossible to decrease (total distance so far + estimated remaining distance) by extending a path to include a neighboring node. (This is analogous to the restriction to nonnegative edge weights in Dijkstra's algorithm.) Monotonicity implies admissibility when the heuristic estimate at any goal node itself is zero, since (letting P = (f,v1,v2,...,vn,g) be a shortest path from any node f to the nearest goal g):
h ( f ) ≤ d ( f , v 1 ) + h ( v 1 ) ≤ d ( f , v 1 ) + d ( v 1 , v 2 ) + h ( v 2 ) ≤ … ≤ L ( P ) + h ( g ) = L ( P ) {\displaystyle h(f)\leq d(f,v_{1})+h(v_{1})\leq d(f,v_{1})+d(v_{1},v_{2})+h(v_{2})\leq \ldots \leq L(P)+h(g)=L(P)}
A* is also optimally efficient for any heuristic h, meaning that no optimal algorithm employing the same heuristic will expand fewer nodes than A*, except when there are multiple partial solutions where h exactly predicts the cost of the optimal path. Even in this case, for each graph there exists some order of breaking ties in the priority queue such that A* examines the fewest possible nodes.
Special cases[edit]
Dijkstra's algorithm, as another example of a uniform-cost search algorithm, can be viewed as a special case of A* where h ( x ) = 0 {\displaystyle h(x)=0} for all x.[4][5] General depth-first search can be implemented using A* by considering that there is a global counter C initialized with a very large value. Every time we process a node we assign C to all of its newly discovered neighbors. After each single assignment, we decrease the counter C by one. Thus the earlier a node is discovered, the higher its h ( x ) {\displaystyle h(x)} value. Both Dijkstra's algorithm and depth-first search can be implemented more efficiently without including a h ( x ) {\displaystyle h(x)} value at each node.
Implementation details[edit]
There are a number of simple optimizations or implementation details that can significantly affect the performance of an A* implementation. The first detail to note is that the way the priority queue handles ties can have a significant effect on performance in some situations. If ties are broken so the queue behaves in a LIFO manner, A* will behave like depth-first search among equal cost paths (avoiding exploring more than one equally optimal solution).
When a path is required at the end of the search, it is common to keep with each node a reference to that node's parent. At the end of the search these references can be used to recover the optimal path. If these references are being kept then it can be important that the same node doesn't appear in the priority queue more than once (each entry corresponding to a different path to the node, and each with a different cost). A standard approach here is to check if a node about to be added already appears in the priority queue. If it does, then the priority and parent pointers are changed to correspond to the lower cost path. A standard binary heap based priority queue does not directly support the operation of searching for one of its elements, but it can be augmented with a hash table that maps elements to their position in the heap, allowing this decrease-priority operation to be performed in logarithmic time. Alternatively, a Fibonacci heap can perform the same decrease-priority operations in constant amortized time.
Admissibility and optimality[edit]
A* is admissible and considers fewer nodes than any other admissible search algorithm with the same heuristic. This is because A* uses an "optimistic" estimate of the cost of a path through every node that it considers—optimistic in that the true cost of a path through that node to the goal will be at least as great as the estimate. But, critically, as far as A* "knows", that optimistic estimate might be achievable.
To prove the admissibility of A*, the solution path returned by the algorithm is used as follows:
When A* terminates its search, it has found a path whose actual cost is lower than the estimated cost of any path through any open node. But since those estimates are optimistic, A* can safely ignore those nodes. In other words, A* will never overlook the possibility of a lower-cost path and so is admissible.
Suppose now that some other search algorithm B terminates its search with a path whose actual cost is not less than the estimated cost of a path through some open node. Based on the heuristic information it has, Algorithm B cannot rule out the possibility that a path through that node has a lower cost. So while B might consider fewer nodes than A*, it cannot be admissible. Accordingly, A* considers the fewest nodes of any admissible search algorithm.
This is only true if both:
A* uses an admissible heuristic. Otherwise, A* is not guaranteed to expand fewer nodes than another search algorithm with the same heuristic.[6]
A* solves only one search problem rather than a series of similar search problems. Otherwise, A* is not guaranteed to expand fewer nodes than incremental heuristic search algorithms.[7]
A* search that uses a heuristic that is 5.0(=ε) times a consistent heuristic, and obtains a suboptimal path.
Bounded relaxation[edit]
While the admissibility criterion guarantees an optimal solution path, it also means that A* must examine all equally meritorious paths to find the optimal path. To compute approximate shortest paths, it is possible to speed up the search at the expense of optimality by relaxing the admissibility criterion. Oftentimes we want to bound this relaxation, so that we can guarantee that the solution path is no worse than (1 + ε) times the optimal solution path. This new guarantee is referred to as ε-admissible.
There are a number of ε-admissible algorithms:
Weighted A*/Static Weighting.[8] If ha(n) is an admissible heuristic function, in the weighted version of the A* search one uses hw(n) = ε ha(n), ε > 1 as the heuristic function, and perform the A* search as usual (which eventually happens faster than using ha since fewer nodes are expanded). The path hence found by the search algorithm can have a cost of at most ε times that of the least cost path in the graph.[9]
Dynamic Weighting[10] uses the cost function f ( n ) = g ( n ) + ( 1 + ε w ( n ) ) h ( n ) {\displaystyle f(n)=g(n)+(1+\varepsilon w(n))h(n)} , where w ( n ) = { 1 − d ( n ) N d ( n ) ≤ N 0 otherwise {\displaystyle w(n)={\begin{cases}1-{\frac {d(n)}{N}}&d(n)\leq N\\0&{\text{otherwise}}\end{cases}}} , and where d ( n ) {\displaystyle d(n)} is the depth of the search and N is the anticipated length of the solution path.
Sampled Dynamic Weighting[11] uses sampling of nodes to better estimate and debias the heuristic error.
A ε ∗ {\displaystyle A_{\varepsilon }^{*}} .[12] uses two heuristic functions. The first is the FOCAL list, which is used to select candidate nodes, and the second hF is used to select the most promising node from the FOCAL list.
Aε[13] selects nodes with the function A f ( n ) + B h F ( n ) {\displaystyle Af(n)+Bh_{F}(n)} , where A and B are constants. If no nodes can be selected, the algorithm will backtrack with the function C f ( n ) + D h F ( n ) {\displaystyle Cf(n)+Dh_{F}(n)} , where C and D are constants.
AlphA*[14] attempts to promote depth-first exploitation by preferring recently expanded nodes. AlphA* uses the cost function f α ( n ) = ( 1 + w α ( n ) ) f ( n ) {\displaystyle f_{\alpha }(n)=(1+w_{\alpha }(n))f(n)} , where w α ( n ) = { λ g ( π ( n ) ) ≤ g ( n ~ ) Λ otherwise {\displaystyle w_{\alpha }(n)={\begin{cases}\lambda &g(\pi (n))\leq g({\tilde {n}})\\\Lambda &{\text{otherwise}}\end{cases}}} , where λ and Λ are constants with λ ≤ Λ {\displaystyle \lambda \leq \Lambda } , π(n) is the parent of n, and ñ is the most recently expanded node.
Complexity[edit]
The time complexity of A* depends on the heuristic. In the worst case of an unbounded search space, the number of nodes expanded is exponential in the depth of the solution (the shortest path) d: O(bd), where b is the branching factor (the average number of successors per state).[15] This assumes that a goal state exists at all, and is reachable from the start state; if it is not, and the state space is infinite, the algorithm will not terminate.
The heuristic function has a major effect on the practical performance of A* search, since a good heuristic allows A* to prune away many of the bd nodes that an uninformed search would expand. Its quality can be expressed in terms of the effective branching factor b*, which can be determined empirically for a problem instance by measuring the number of nodes expanded, N, and the depth of the solution, then solving[16]
N + 1 = 1 + b ∗ + ( b ∗ ) 2 + ⋯ + ( b ∗ ) d . {\displaystyle N+1=1+b^{*}+(b^{*})^{2}+\dots +(b^{*})^{d}.}
Good heuristics are those with low effective branching factor (the optimal being b* = 1).
The time complexity is polynomial when the search space is a tree, there is a single goal state, and the heuristic function h meets the following condition:
| h ( x ) − h ∗ ( x ) | = O ( log  h ∗ ( x ) ) {\displaystyle |h(x)-h^{*}(x)|=O(\log h^{*}(x))}
where h* is the optimal heuristic, the exact cost to get from x to the goal. In other words, the error of h will not grow faster than the logarithm of the "perfect heuristic" h* that returns the true distance from x to the goal.[9][15]
Applications[edit]
A* is commonly used for the common pathfinding problem in applications such as games, but was originally designed as a general graph traversal algorithm.[3] It finds applications to diverse problems, including the problem of parsing using stochastic grammars in NLP.[17] Other cases include an Informational search with online learning.[18]
Relations to other algorithms[edit]
What sets A* apart from a greedy best-first search algorithm is that it takes the cost/distance already traveled, g(n), into account.
Some common variants of Dijkstra's algorithm can be viewed as a special case of A* where the heuristic h ( n ) = 0 {\displaystyle h(n)=0} for all nodes;[4][5] in turn, both Dijkstra and A* are special cases of dynamic programming.[19] A* itself is a special case of a generalization of branch and bound[20] and can be derived from the primal-dual algorithm for linear programming.[21]
Variants[edit]
Incremental heuristic search
Anytime Repairing A* (ARA*)[22]
Block A*
D*
Field D*
Fringe
Fringe Saving A* (FSA*)
Generalized Adaptive A* (GAA*)
IDA*
Informational search[18]
Jump point search
Lifelong Planning A* (LPA*)
Simplified Memory bounded A* (SMA*)
Theta*
Anytime A*[23]
Realtime A*[24]
Anytime Dynamic A*
Time-Bounded A* (TBA*)[25]
A* can also be adapted to a bidirectional search algorithm. Special care needs to be taken for the stopping criterion.[26]
See also[edit]
Pathfinding
Breadth-first search
Depth-first search
Any-angle path planning, search for paths that are not limited to move along graph edges but rather can take on any angle
Notes[edit]
Jump up ^ Goal nodes may be passed over multiple times if there remain other nodes with lower f values, as they may lead to a shorter path to a goal.
References[edit]
Jump up ^ Delling, D.; Sanders, P.; Schultes, D.; Wagner, D. (2009). Algorithmics of Large and Complex Networks: Design, Analysis, and Simulation. Springer. pp. 11个$7–139. doi:10.1007/978-3-642-02094-0_7.
Jump up ^ Zeng, W.; Church, R. L. (2009). "Finding shortest paths on real road networks: the case for A*". International Journal of Geographical Information Science. 23 (4): 531–543. doi:10.1080/13658810801949850.
^ Jump up to: a b Hart, P. E.; Nilsson, N. J.; Raphael, B. (1968). "A Formal Basis for the Heuristic Determination of Minimum Cost Paths". IEEE Transactions on Systems Science and Cybernetics SSC4. 4 (2): 100–107. doi:10.1109/TSSC.1968.300136.
^ Jump up to: a b De Smith, Michael John; Goodchild, Michael F.; Longley, Paul (2007), Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools, Troubadour Publishing Ltd, p. 344, ISBN 9781905886609 .
^ Jump up to: a b Hetland, Magnus Lie (2010), Python Algorithms: Mastering Basic Algorithms in the Python Language, Apress, p. 214, ISBN 9781430232377 .
Jump up ^ Dechter, Rina; Judea Pearl (1985). "Generalized best-first search strategies and the optimality of A*". Journal of the ACM. 32 (3): 505–536. doi:10.1145/3828.3830.
Jump up ^ Koenig, Sven; Maxim Likhachev; Yaxin Liu; David Furcy (2004). "Incremental heuristic search in AI". AI Magazine. 25 (2): 99–112.
Jump up ^ Pohl, Ira (1970). "First results on the effect of error in heuristic search". Machine Intelligence. 5: 219–236.
^ Jump up to: a b Pearl, Judea (1984). Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley. ISBN 0-201-05594-5.
Jump up ^ Pohl, Ira (August 1973). "The avoidance of (relative) catastrophe, heuristic competence, genuine dynamic weighting and computational issues in heuristic problem solving" (PDF). Proceedings of the Third International Joint Conference on Artificial Intelligence (IJCAI-73). 3. California, USA. pp. 11–17.
Jump up ^ Köll, Andreas; Hermann Kaindl (August 1992). "A new approach to dynamic weighting". Proceedings of the Tenth European Conference on Artificial Intelligence (ECAI-92). Vienna, Austria. pp. 16–17.
Jump up ^ Pearl, Judea; Jin H. Kim (1982). "Studies in semi-admissible heuristics". IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). 4 (4): 392–399.
Jump up ^ Ghallab, Malik; Dennis Allard (August 1983). "Aε – an efficient near admissible heuristic search algorithm" (PDF). Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83). 2. Karlsruhe, Germany. pp. 789–791. Archived from the original (PDF) on 2014-08-06.
Jump up ^ Reese, Bjørn (1999). "AlphA*: An ε-admissible heuristic search algorithm".
^ Jump up to: a b Russell, Stuart; Norvig, Peter (2003) [1995]. Artificial Intelligence: A Modern Approach (2nd ed.). Prentice Hall. pp. 97–104. ISBN 978-0137903955.
Jump up ^ Russell, Stuart; Norvig, Peter (2009) [1995]. Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. p. 103. ISBN 978-0-13-604259-4.
Jump up ^ Klein, Dan; Manning, Christopher D. (2003). A* parsing: fast exact Viterbi parse selection. Proc. NAACL-HLT.
^ Jump up to: a b Kagan E. and Ben-Gal I. (2014). "A Group-Testing Algorithm with Online Informational Learning" (PDF). IIE Transactions, 46:2, 164-184,.
Jump up ^ Ferguson, Dave; Likhachev, Maxim; Stentz, Anthony (2005). A Guide to Heuristic-based Path Planning (PDF). Proc. ICAPS Workshop on Planning under Uncertainty for Autonomous Systems.
Jump up ^ Nau, Dana S.; Kumar, Vipin; Kanal, Laveen (1984). "General branch and bound, and its relation to A∗ and AO∗" (PDF). Artificial Intelligence. 23 (1): 29–58. doi:10.1016/0004-3702(84)90004-3.
Jump up ^ Ye, Xugang; Han, Shih-Ping; Lin, Anhua (2010). "A Note on the Connection Between the Primal-Dual and the A* Algorithm". Int'l J. Operations Research and Information Systems. 1 (1): 73–85.
Jump up ^ Likhachev, Maxim; Gordon, Geoff; Thrun, Sebastian. "ARA*: Anytime A* search with provable bounds on sub-optimality". In S. Thrun, L. Saul, and B. Schölkopf, editors, Proceedings of Conference on Neural Information Processing Systems (NIPS), Cambridge, MA, 2003. MIT Press.
Jump up ^ Hansen, Eric A., and Rong Zhou. "Anytime Heuristic Search." J. Artif. Intell. Res.(JAIR) 28 (2007): 267-297.
Jump up ^ Korf, Richard E. "Real-time heuristic search." Artificial intelligence 42.2-3 (1990): 189-211.
Jump up ^ Björnsson, Yngvi; Bulitko, Vadim; Sturtevant, Nathan (July 11–17, 2009). TBA*: time-bounded A* (PDF). IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence. Pasadena, California, USA: Morgan Kaufmann Publishers Inc. pp. 431–436.
Jump up ^ "Efficient Point-to-Point Shortest Path Algorithms" (PDF). from Princeton University
Further reading[edit]
Hart, P. E.; Nilsson, N. J.; Raphael, B. (1972). "Correction to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths"". SIGART Newsletter. 37: 28–29. doi:10.1145/1056777.1056779.
Nilsson, N. J. (1980). Principles of Artificial Intelligence. Palo Alto, California: Tioga Publishing Company. ISBN 0-935382-01-1.
External links[edit]
Clear visual A* explanation, with advice and thoughts on path-finding
Variation on A* called Hierarchical Path-Finding A* (HPA*)
Retrieved from "https://en.wikipedia.org/w/index.php?title=A*_search_algorithm&oldid=843997285"
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Joy to the World
From Wikipedia, the free encyclopedia
Jump to: navigation, search
This article is about the Christmas carol. For other uses, see Joy to the World (disambiguation).
Joy to the World
Text
Isaac Watts,
Language
English
Published
1719
"Joy to the World" is a popular Christmas carol[1] with words by Isaac Watts. As of the late 20th century, "Joy to the World" was the most-published Christmas hymn in North America.[1]
Contents
[hide]
1 Origin
2 Music
3 Words
4 Lyric variants
5 Recordings
6 References
7 External links
Origin[edit]
The words of the hymn are by English writer Isaac Watts, based on Psalm 98, 96:11-12 and Genesis 3:17-18, in the Bible. The song was first published in 1719 in Watts' collection; The Psalms of David: Imitated in the language of the New Testament, and applied to the Christian state and worship. The paraphrase is Watts' Christological interpretation. Consequently, he does not emphasize with equal weight the various themes of Psalm 98. In stanzas 1 and 2 Watts writes of heaven and earth rejoicing at the coming of the King. An interlude that depends more on Watts' interpretation than the psalm text, stanza 3 speaks of Christ's blessings extending victoriously over the realm of sin. The cheerful repetition of the non-psalm phrase "far as the curse is found" has caused this stanza to be omitted from some hymnals. But the line makes joyful sense when understood from the New Testament eyes through which Watts interprets the psalm. Stanza 4 celebrates Christ's rule over the nations.".[2] The nations are called to celebrate because God's faithfulness to the house of Israel has brought salvation to the world.[3]
Music[edit]
Watts' 1719 preface says the verses "..are fitted to the Tunes of the Old PSALM-BOOK" and includes the instruction "sing all entitled COMMON METER".[4] In the late 1700's Joy to the World was printed together with music several times. [5]
The version of this carol usually heard today is from the edition by Lowell Mason for The National Psalmist (Boston, 1848),[6] his fourth revision of the tune he named ANTIOCH and attributed as "arranged from Handel". This tune has the first four notes in common with the chorus Lift up your heads from Messiah (premiered 1742), and the third line recalls the arioso Comfort ye from the same oratorio, but this resemblance is dismissed as 'chance resemblance' by Handel scholars today.[7] A 1986 article by John Wilson showed ANTIOCH's close resemblance to a predecessor titled COMFORT and associated with Wesley's hymn "O Joyful Sound", with one publication firmly dated 1833, three years earlier than Mason's first version.[8]
Words[edit]
The text appears thus in The Psalms of David : imitated in the Language of the New Testament, and apply'd to the Christian State and Worship] (London 1719):
Psalm XCVIII. Second Part.
The Messiah's Coming and Kingdom

Joy to the World; The Lord is come;
Let Earth receive her King:
Let every Heart prepare him Room,
And Heaven and Nature sing.

Joy to the Earth, The Saviour reigns;
Let Men their Songs employ;
While Fields & Floods, Rocks, Hills & Plains
Repeat the sounding Joy.

No more let Sins and Sorrows grow,
Nor Thorns infest the Ground:
He comes to make his Blessings flow
Far as the Curse is found.

He rules the World with Truth and Grace,
And makes the Nations prove
The Glories of his Righteousness,
And Wonders of his Love.
Lyric variants[edit]
In the Latter-day Saint hymnal, the refrain in the first verse is "And Saints and Angels Sing" (see Joy to the World (Phelps)).
Modern recordings, including those aimed at children such as VeggieTales (part of A Very Veggie Christmas, The Singing Christmas Tree) and Disney Sing-Along versions, often omit the third verse.
Recordings[edit]
Joy to the World
Choral performance of "Joy to the World" by United States Army Chorus
Problems playing this file? See media help.
A version by the Trinity Choir was very popular in 1911[9] and the carol has since been recorded by many artists including Andy Williams, The Supremes, Bing Crosby, Ella Fitzgerald, Johnny Cash, Nat King Cole, Walter Cherry, Neil Diamond, Pat Boone, Perry Como, Vic Damone and Mariah Carey.
As of the late 20th century, "Joy to the World" was the most-published Christmas hymn in North America.[1]
References[edit]
^ Jump up to: a b c It was published in 1,387 hymnals in North America before 1979, as recorded in the Dictionary of North American Hymnology. Top 20 Christmas hymns cited at Hymnary.org.
Jump up ^ Joy to the world! the Lord is come! at Hymnary.org
Jump up ^ Joy to the World!, Worship Leader magazine (archive.org, 18 July 2011)
Jump up ^ Watts 1719, p. xxxii. it is not clear whether "Old Psalm-book" refers to Playford's 1677 publication or some other.
Jump up ^ For example to the tune WARSAW in Samuel Holyoke's Harmonia Americana, 1791 (page 87)
Jump up ^ Celebrate, Rejoice and Sing: Christmas Music in America, Roger L. Hall, PineTree Press, 2003, p 8; see also >https://hymnary.org/text/joy_to_the_world_the_lord_is_come for usage of ANTIOCH and other tunes.
Jump up ^ The Cambridge Handel Encyclopedia, Annette Landgraf, David Vickers, Cambridge University Press, 26 November 2009. 'Joy to the World' entry by Nicholas Temperley
Jump up ^ The Origins of the Tune "Antioch", Bulletin No. 166 of the Hymn Society of Great Britain and Ireland; a summary is given in The Oxford Book of Carols 1994, p.273
Jump up ^ Whitburn, Joel (1986). Joel Whitburn's Pop Memories 1890-1954. Wisconsin, USA: Record Research Inc. p. 422. ISBN 0-89820-083-0.
External links[edit]
United States portal
Music portal
Christmas portal
Free sheet music of "Joy to the World" for SATB from Cantorion.org
Joy to the World - Early New England Christmas Carol
"Joy to the World" played on a Hammond Organ
Retrieved from "https://en.wikipedia.org/w/index.php?title=Joy_to_the_World&oldid=823370277"
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