Project description
Uncovering ways to scale projection-free continuous optimisation methods
Over the past decades, convex optimisation – a class of continuous optimisation including least squares methods – has proven invaluable to science and engineering disciplines. More recently, it has been implemented with great success to support the booming use of Big Data and AI. Computationally efficient (projection-free) methods have been particularly useful, but scaling them has been complicated due to limited optimisation oracle complexity. The ERC-funded ProFreeOpt project aims to study a variety of such oracles for insight into how simple and effective optimisation steps can produce projection-free oracles on par with projection-based ones. This foundational work could lead to a new era of continuous optimisation in the era of Big Data and AI.
Objective
Efficient algorithms for continuous and in particular convex optimization have revolutionized science and engineering in the past decades, providing the engine that drives numerous key technical and computational practices used across almost every scientific and engineering discipline.
In particular, it is one of the main pillars of the ongoing data science and AI revolution. For many important large scale optimization problems that include constraints that are complex yet highly-structured, the algorithmic weapon of choice are so-called projection-free methods, which are mostly based on the classical Frank-Wolfe method.
Despite vast interest and progress in recent years on scaling up projection-free methods, their optimization oracle complexity remains significantly inferior to their projection-based counterparts. This shortcoming prevails throughout almost every optimization paradigm of interest and is often the bottleneck in further scaling-up this important family of optimization methods.
The overarching goal of this project is the foundational and systematic study of the landscape of continuous optimization with computationally efficient (projection-free) optimization oracles. My goal is to develop an understanding, across a variety of central optimization paradigms, of how far can we push methods that rely only on simple and efficient optimization steps towards matching the complexities of state-of-the-art projection-based methods (that rely on computationally-expensive optimization steps). I envision that the novel algorithmic and methodological results that will come out of this research will lay the foundations to a new generation of far more advanced algorithms for continuous optimization with structured constraints. These will allow to tackle on a practical scale a much richer variety of optimization settings, and will be built to leverage, in a more specialized fine-grained manner, favorable and plausible structure of optimization problems.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
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Call for proposal
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(opens in new window) ERC-2024-COG
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32000 Haifa
Israel
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