Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Kibaek Kim Project Name: NEXTGENOPT Division: MCS Project title: Next generation optimization Associated funding: Office of Science Other Systems: Science: Advances in algorithms for stochastic optimization (SO) and uncertainty quantification (UQ) are critical for the efficient design and real-time optimization of next-generation national infrastructures. Anticipating and mitigating uncertainty of weather, demands, and contingencies in a more integrated environment is necessary to mitigate market volatility and prevent cascading failures that can ultimately lead to catastrophic shortages of supply. The integrated monitoring and optimization of infrastructures systems will generate huge amounts of data that cannot be possibly processed without exploiting underlying probabilistic structures (e.g., sparsity in spatio-temporal correlation patterns) and without exploiting the properties of the particular decision-making objective at hand (e.g., stochastic, PDEs). It is thus at the heart of our current research the philosophy that a structure-oriented and integrated approach to UQ/SO is necessary. Project description: We will develop scalable decomposition methods for large-scale structured optimization problems. Applications include energy infrastructure design, planning, and operations. We will use existing software packages DSP and PIPS, developed by Argonne-MCS. The software packages are open-source and written in C++ with MPI library. The scalability results for the existing algorithms in DSP and PIPS have been reported in papers. In FY18, several experiments are planned as follows: 1. Migration and testing from Blues to Bebob. (Q1: 10/1 – 12/31) 2. Developing branch-and-bound method on top of dual decomposition for stochastic mixed-integer programming (SMIP) problems. (Q1) 3. Developing network decomposition method to unit commitment of a large power grid network. (Q1 and Q2) 4. Creating and evaluating a number of test instances for SMIP problems. (Q1 and Q2) 5. Developing asynchronous level-bundle method for SMIP problems. (Q3 and Q4) 6. Prototyping scalable decomposition algorithms for bilevel optimization problems. (Q3 and Q4) The number of cores used in each run varies from tens to more than a thousand, depending on the experiments. Industry partnership: Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 180000 Q1: 80000 Q2: 50000 Q3: 20000 Q4: 30000 Justification: Storage requirements: Thank You, The LCRC Accounts System