Solving Traveling Salesman Problem (TSP) is NP-hard yet fundamental for wide real-world applications. Classical exact methods face challenges in scaling, and heuristic methods often require domain-specific parameter calibration. While learning-based approaches have shown promise, they suffer from poor generalization and limited scalability due to fixed training data. This work proposes ViTSP, a novel framework that leverages pre-trained vision language models (VLMs) to visually guide the solution process for large-scale TSPs. The VLMs function to identify promising small-scale subproblems from a visualized TSP instance, which are then efficiently optimized using an off-the-shelf solver to improve the global solution. ViTSP bypasses the dedicated model training at the user end while maintaining effectiveness across diverse instances. Experiments on real-world TSP instances ranging from 1k to 88k nodes demonstrate that ViTSP consistently achieves solutions with average optimality gaps below 0.2%, outperforming existing learning-based methods. Under the same runtime budget, it surpasses the best-performing heuristic solver, LKH-3, by reducing its gaps by 12% to 100%, particularly on very-large-scale instances with more than 10k nodes. Our framework offers a new perspective in hybridizing pre-trained generative models and operations research solvers in solving combinatorial optimization problems, with practical implications for integration into more complex logistics systems. The code is available at https://anonymous.4open.science/r/ViTSP_codes-6683.

Cycle-level simulators such as gem5 are widely used in microarchitecture design, but they are prohibitively slow for large-scale design space explorations. We present Concorde, a new methodology for learning fast and accurate performance models of microarchitectures. Unlike existing simulators and learning approaches that emulate each instruction, Concorde predicts the behavior of a program based on compact performance distributions that capture the impact of different microarchitectural components. It derives these performance distributions using simple analytical models that estimate bounds on performance induced by each microarchitectural component, providing a simple yet rich representation of a program's performance characteristics across a large space of microarchitectural parameters. Experiments show that Concorde is more than five orders of magnitude faster than a reference cycle-level simulator, with about 2% average Cycles-Per-Instruction (CPI) prediction error across a range of SPEC, open-source, and proprietary benchmarks. This enables rapid design-space exploration and performance sensitivity analyses that are currently infeasible, e.g., in about an hour, we conducted a first-of-its-kind fine-grained performance attribution to different microarchitectural components across a diverse set of programs, requiring nearly 150 million CPI evaluations.

In this paper, we provide a novel strategy for solving Traveling Salesman Problem, which is a famous combinatorial optimization problem studied intensely in the TCS community. In particular, we consider the imitation learning framework, which helps a deterministic algorithm making good choices whenever it needs to, resulting in a speed up while maintaining the exactness of the solution without suffering from the unpredictability and a potential large deviation. Furthermore, we demonstrate a strong generalization ability of a graph neural network trained under the imitation learning framework. Specifically, the model is capable of solving a large instance of TSP faster than the baseline while has only seen small TSP instances when training.

Just how much will AI influence tomorrow's consumer economy? Will it know much we like avocado? When you talk to the average person about harnessing AI, many don't consider these questions. They are prone to offload fears of Skynet rather than contemplate how this tech will be used in the real world, as in our sushi bar example. Those with a little more subject matter knowledge may point to purely digital applications, such as social media platforms identifying terrorist content sans human intervention, or drug companies using machine learning to sift through mountains of health data for tomorrow's cures.

Predicting and comparing algorithm performance on graph instances is challenging for multiple reasons. First, there is usually no standard set of instances to benchmark performance. Second, using existing graph generators results in a restricted spectrum of difficulty and the resulting graphs are usually not diverse enough to draw sound conclusions. That is why recent work proposes a new methodology to generate a diverse set of instances by using an evolutionary algorithm. We can then analyze the resulting graphs and get key insights into which attributes are most related to algorithm performance. We can also fill observed gaps in the instance space in order to generate graphs with previously unseen combinations of features. This methodology is applied to the instance space of the Hamiltonian completion problem using two different solvers, namely the Concorde TSP Solver and a multi-start local search algorithm.

The Clustered Traveling Salesman Problem (CTSP) is a variant of the popular Traveling Salesman Problem (TSP) arising from a number of real-life applications. In this work, we explore an uncharted solution approach that solves the CTSP by transforming it to the well-studied TSP. For this purpose, we first investigate a technique to convert a CTSP instance to a TSP and then apply popular TSP solvers (including exact and heuristic solvers) to solve the resulting TSP instance. We want to answer the following questions: How do state-of-the-art TSP solvers perform on clustered instances converted from the CTSP? Do state-of-the-art TSP solvers compete well with the best performing methods specifically designed for the CTSP? For this purpose, we present intensive computational experiments on various CTSP benchmark instances to draw conclusions.

Commercial supersonic air travel could return in just half a decade after a Richard Branson-backed company aiming to replace the concord announced it will begin test flights next year. Boom Supersonic has said initial test flights for its 1,451mph (2,330kph) aircraft, nicknamed the'baby boom', will begin by the end of 2018, with both subsonic and supersonic tests taking place in the US. Supersonic flight tests will be conducted near Edwards Air Force Base in Southern California in partnership with Virgin Galactic's The Spaceship Company. If the firm's full-sized, 55-seater aircraft is approved, the first passengers could be making a supersonic journey across the Atlantic by 2023 at a top speed more than 100mph (160km/h) faster than the infamous Concorde. Boom Supersonic has said initial test flights for its 1,451mph (2,330kph) aircraft, nicknamed the'baby boom', will begin by the end of 2018, with both subsonic and supersonic tests taking place in the US.

There are flying cars and Concorde's would-be supersonic successor, a company offering to deliver cargo to the Moon - for a mere $1.2 million per kilogram - and the latest in funky futuristic aviation ideas, both big and small. No doubt about it: the Paris Air Show is an aerospace geek's paradise. But with everything from the smallest drones to the largest passenger jets on display, it's tough to sift through it all. So here's a guide to some of the cool things that caught our eye this week. Visitors looks at the flying car Pegasus 1, built by French entrepreneur Jerome Dauffy at Paris Air Show, in Le Bourget, east of Paris, France, Tuesday, June 20, 2017 in Paris.