# Combinatorial optimization Published articles for Combinatorial optimization. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Quantum-Augmented Applications: Integrating Quantum Subroutines into Classical Software Stacks DevFeed: [Quantum-Augmented Applications: Integrating Quantum Subroutines into Classical Software Stacks]() Original publisher: [Read original article]() Author: Dr. Ahmad Mateen Ishanzai Published: 2026-08-20T18:43:39Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Software](), [Optimization](), [Hardware](), [coding](), [Network]() Tags: [buiilding-software](), [cc-by-sa](), [combinatorial-optimization](), [contributed](), [high-performance-computing](), [latency](), [low-latency](), [optimization](), [quantum](), [quantum-computing]() ### AI overview This article presents quantum-augmented applications as hybrid systems that integrate Quantum Processing Units with classical software pipelines. It describes delegating targeted computationally difficult subroutines to QPUs while retaining classical business logic, data preprocessing, orchestration, and optimization, and discusses practical constraints including noise, transpilation latency, and network overhead. ### Source excerpt Founded in 2008, Stack Overflow's public platform is used by nearly everyone who codes to learn, share their knowledge, collaborate, and build their careers. ## Navigating uncertainty in Amazon's middle-mile network DevFeed: [Navigating uncertainty in Amazon's middle-mile network]() Original publisher: [Read original article]() Author: Ruth Misener; Hana Ku; Georgios Paschos Published: 2026-05-06T13:37:38Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [amazon](), [Network design](), [Optimization](), [Network](), [networking]() Tags: [amazon](), [combinatorial-optimization](), [demand-forecasting](), [efficiency](), [middle-mile](), [network-design](), [operations-research-and-optimization](), [optimization](), [scot](), [supply-chain](), [supply-chain-optimization-technologies-scot](), [transportation-planning]() ### AI overview Amazon engineers and scientists describe how they optimize the company's middle-mile delivery network under uncertainty. The article covers demand and travel-time variability, network design, routing, inventory positioning, and mixed-integer optimization across many facilities and products. ### Source excerpt Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty -- and keep them adapting without missing a beat. ## Accelerating Mathematical and Scientific Discovery with Gemini Deep Think DevFeed: [Accelerating Mathematical and Scientific Discovery with Gemini Deep Think]() Original publisher: [Read original article]() Author: Thang Luong; Vahab Mirrokni Published: 2026-02-09T16:12:06Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Machine Learning & Artificial Intelligence](), [Combinatorial optimization](), [Algorithms](), [Mathematics](), [Optimization](), [Computer science](), [Training AI Models](), [Streams](), [data](), [Network]() Tags: [ai](), [algorithms](), [combinatorial-optimization](), [data](), [gemini](), [optimization](), [physics](), [research](), [science](), [streams](), [training]() ### AI overview An advanced version of Gemini Deep Think helped researchers resolve long-standing problems across algorithms, machine learning optimization, combinatorial optimization, economics, and physics. The article highlights new counterexamples, mathematical explanations for AI training techniques, an extension of an auction theorem to real-valued bids, and a closed-form solution for integrals involving cosmic-string singularities. ### Source excerpt Research papers point to the growing impact of Deep Think across fields ## What could quantum computing do for Automotive Companies? DevFeed: [What could quantum computing do for Automotive Companies?]() Original publisher: [Read original article]() Author: Bahram Ganjipour Published: 2024-01-29T08:05:33Z Content type: article Language: en Sources: [Volvo Cars Engineering - Medium]() Topics: [Quantum Computing](), [Combinatorial optimization](), [datasets]() Tags: [algorithms](), [automotive](), [automotive-industry](), [combinatorial-optimization](), [ev-battery](), [machine-learning](), [quantum-computing](), [simulation]() ### AI overview The article discusses potential real-world uses of quantum computing for automotive companies, including constrained combinatorial optimization, quantum algorithms compatible with NISQ hardware, quantum programming, and simulation. It also describes how machine learning and large datasets relate to data-intensive business problems, while noting current limitations and the need to compare quantum and classical performance. ### Source excerpt In the final post of this series on the rise of quantum computing, Bahram Ganjipour, a senior software engineer and quantum computing lead at Volvo Cars, goes over some of the real-world use cases of this fast-moving technology -- as well as the current limitations on its potential. What exactly can quantum computing assist us with? As highlighted in the first post, one of the main hurdles for any organization when it comes to quantum computing (QC) is the need to comprehend the business potential of quantum computing. This understanding is crucial for making informed decisions regarding the adoption of quantum computing technologies. Early adoption of quantum computing can help companies in navigating this complex landscape and distinguishing between hype and real opportunities. By embracing QC early on, organizations can gain a competitive edge and position themselves at the forefront of quantum advancements. Real-world use cases of quantum computing and Potential game-changer: When it comes to tackling real-world business problems using quantum computing we need to consider several elements listed below: - How to map a business use case into a format that can be handled on quantum hardware. Constrained combinatorial optimization problems may be encoded in a constraint-free form. - Being aware of and selecting quantum algorithms compatible with NISQ hardware. - Developing programming skills for quantum devices. Both direct proficiency in a certain language and conceptual understanding of how to think when developing quantum algorithms. - Simulation/execution, and what you can learn from it, such as how quantum performance compares to classical performance and the potential for mid-term, non-trivial achievements. o Machine Learning and large datasets The concept should be of particular interest to anyone who works with data science at scale. Machine learning (ML) can be immensely valuable in analyzing massive amounts of data as handling the complexity of analyzing l ## Linear Programming and Healthy Diets -- Part 1 DevFeed: [Linear Programming and Healthy Diets -- Part 1]() Original publisher: [Read original article]() Published: 2014-06-02T09:00:27Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Optimization](), [Combinatorial optimization](), [Mathematics](), [Programming](), [Computer science]() Tags: [combinatorial-optimization](), [duality](), [linear-programming](), [mathematics](), [nutrition](), [operations-research](), [optimization](), [primal](), [programming](), [real-world]() ### AI overview This introductory tutorial explains linear programming as a central problem in combinatorial optimization. It models choosing quantities of oranges, milk, and broccoli to minimize food costs while meeting nutritional constraints, including recommended levels of water, calcium, and vitamin C. ### Source excerpt Optimization is by far one of the richest ways to apply computer science and mathematics to the real world. Everybody is looking to optimize something: companies want to maximize profits, factories want to maximize efficiency, investors want to minimize risk, the list just goes on and on. The mathematical tools for optimization are also some of the richest mathematical techniques. They form the cornerstone of an entire industry known as operations research, and advances in this field literally change the world.