This book constitutes the proceedings of the 22nd Conference on Integer Programming and Combinatorial Optimization, IPCO 2021, which took place during May 19-21, 2021. The conference was organized by Georgia Institute of Technology and planned to take place it Atlanta, GA, USA, but changed to an online format due to the COVID-19 pandemic. The 33 papers included in this book were carefully reviewed and selected from 90 submissions. IPCO is under the auspices of the MathematicalOptimization Society, and it is an important forum for presenting the latest results of theory and practice of the various aspects of discrete optimization.

Metallic starch capped gold nanoparticles have been synthesized by the reduction of chloroaurate anions AuCl4]- solution with hydrazine in the aqueous starch and ethylene glycol solution at room temperature and at atmospheric pressure. The characterization of synthesized gold nanoparticles by UV-Vis spectroscopy, Scanning electron microscopy (SEM), X-ray diffraction (XRD) indicate that average size of pure gold NPs is 5-10 nm, they are spherical in shape and are pure metallic gold. Starch capped gold nanoparticles along with tyrosinase enzyme are effectively entrapped within sol-gel matrix for application in catechol biosensor. Enzymatic reaction between tyrosinase and catechol results in the formation of a colored compound which shows characteristic absorbance at 410 nm. Starch@AuNPs based catechol biosensor showed fast response with linearity for catechol sensing from 1 to 5 mM. Effect of buffer pH and its concentration was also studied and results showed drastic changes in absorption intensity with pH and concentration variation. Along with catechol biosensor, starch capped gold NPs find their application in glucose, urease and peroxide biosensor, owing to their stability.

With the advent of approximation algorithms for NP-hard combinatorial optimization problems, several techniques from exact optimization such as the primal-dual method have proven their staying power and versatility. This book describes a simple and powerful method that is iterative in essence, and similarly useful in a variety of settings for exact and approximate optimization. The authors highlight the commonality and uses of this method to prove a variety of classical polyhedral results on matchings, trees, matroids, and flows. The presentation style is elementary enough to be accessible to anyone with exposure to basic linear algebra and graph theory, making the book suitable for introductory courses in combinatorial optimization at the upper undergraduate and beginning graduate levels. Discussions of advanced applications illustrate their potential for future application in research in approximation algorithms.

With the advent of approximation algorithms for NP-hard combinatorial optimization problems, several techniques from exact optimization such as the primal-dual method have proven their staying power and versatility. This book describes a simple and powerful method that is iterative in essence, and similarly useful in a variety of settings for exact and approximate optimization. The authors highlight the commonality and uses of this method to prove a variety of classical polyhedral results on matchings, trees, matroids, and flows. The presentation style is elementary enough to be accessible to anyone with exposure to basic linear algebra and graph theory, making the book suitable for introductory courses in combinatorial optimization at the upper undergraduate and beginning graduate levels. Discussions of advanced applications illustrate their potential for future application in research in approximation algorithms.