The Journey of the Traveling Salesman Problem

Hilaal Alam
4 min readJul 16



In 1962, Procter & Gamble unveiled a captivating challenge, offering a grand prize of $10,000 — an enticing sum capable of buying a house — to anyone who could crack the elusive Traveling Salesman Problem (TSP). This mathematical puzzle, which involved finding the shortest route to visit 33 cities in the USA, instantly fascinated the minds of mathematicians and computer scientists. As the TSP’s complexity posed a daunting challenge, ingenious minds embarked on a remarkable journey of algorithms and computational breakthroughs. Let’s embark on an enthralling expedition into the captivating world of the TSP and those intrepid adventurers who sought to conquer it.

A Conundrum of Cosmic Proportions:

The TSP presents a mind-boggling puzzle with an astronomical number of potential routes. With 33 cities, the number of possible options reaches a staggering 131 sextillion — a figure that exceeds the grasp of our imagination. Even with the most powerful computers at our disposal, attempting to manually calculate the shortest path would span trillions of years. Consequently, many believed the problem to be insurmountable.

The Pioneering Pilots:

The TSP’s origins trace back to 1930 when Austrian mathematician Karl Menger first recognized its perplexing nature. However, it wasn’t until 1954 that a brave team led by George Dantzig, Ray Fulkerson, and Seller Johnson dared to tackle a similar problem involving 48 states and Washington DC. Employing linear programming, they achieved a feat previously deemed impossible, finding a route covering 49 cities spanning an impressive 12,345 miles. Yet, the true enigma of the TSP remained unsolved.

George Dantzig, Ray Fulkerson, and Seller Johnson (

A Frenzied Race to the Finale:

Numerous contenders emerged, presenting potential solutions for Procter & Gamble’s 33-city TSP challenge. However, it was Robert Karg and Gerald Thompson who astounded all with their hit-or-miss strategy, vying for victory. In the ultimate showdown, Thompson emerged triumphant, securing the coveted grand prize. Yet, the pursuit of a comprehensive algorithm persisted, driving adventurers further into the abyss of this tantalizing puzzle.

Optimal Tour of 33-City (

Quest for the Algorithmic Grail:

Jack Edmonds, a wise sage, postulated the existence of a “good” algorithm — an approach capable of cracking problems within a reasonable timeframe. The key lies in runtime, determined by the number of operations required. Problems solvable in polynomial time are known as P-type problems, while those with exponential complexity fall under the non-deterministic polynomial (NP) category. The tantalizing question of whether P equals NP remains unanswered, offering a million-dollar reward to the daring soul who unveils the truth. The TSP, residing in the NP-complete realm, unlocks the door to a class of interconnected problems.

P vs NP

Navigating Algorithmic Breakthroughs:

In 1971, the brilliant minds at IBM, Held and Karp, forged an algorithm that conquered a TSP with 64 points — an achievement unmatched for years. Riding the wave of innovation, Panagiotis Miliotis surpassed their milestone, utilizing Dantzig’s approach to uncover the shortest route among 80 randomly chosen points. The era belonged to Grotschel and Padberg, who dominated the TSP for fifteen glorious years. In 1977, they unraveled the 120-city problem, while their partnership with IBM researcher Harlan Crowder led to a solution for 318 cities in 1987. Grotschel and Holland, joined by Padberg and Rinaldi, confronted challenges involving 532 US cities, 666 global locations, 1,002 urban centers, and 2,392 interconnected metropolises.

The Ascendancy of Parallel Computing:

In 1992, Chvatal and W J Cook harnessed the power of parallel computer networks, enabling them to conquer TSP problems encompassing 3,038 cities. The birth of the Concorde algorithm marked a turning point, empowering them to embark on even more audacious expeditions. In 1998, they charted a route through 13,509 cities across the US. In 2004, they triumphed over Sweden’s 24,978-city tour, and in 2006, they audaciously tackled an awe-inspiring 85,900-city journey. The Concorde algorithm found purpose beyond mathematics, revolutionizing the fabrication of customized chips with laser-cut precision.

Defying Limits:

In 2010, Danish computer scientist Keld Helsgaun etched his name in the annals of TSP history. By solving the problem for an astonishing 1,904,711 cities worldwide, including Antarctica’s research labs, Helsgaun shattered previous records. The optimal path, spanning a distance of 7,515,790,345 meters, came within a hair’s breadth of perfection, with the Concorde code calculating a path only 0.0476% longer. Similarly, artist Yuichi Nagata unveiled a mesmerizing continuous line artwork reminiscent of the Mona Lisa, composed of 100,000 points, surpassing perfection by a mere 0.003%.

Mona Lisa with TSP (


The Traveling Salesman Problem, a perplexing enigma that has intrigued generations, has spurred extraordinary advancements in algorithmic efficiency and parallel computing. Though the ultimate optimal solution remains tantalizingly out of reach, the journey has expanded our horizons, unlocking new realms of computational prowess. The TSP serves as a testament to humanity’s insatiable curiosity, our unwavering pursuit of the perfect path, and the remarkable feats we can achieve when faced with insurmountable complexity. As we continue to unravel the mysteries of the TSP, we inch ever closer to the triumph of the optimal journey.



Hilaal Alam

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