Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider two classic problems in convex optimization: 1) minimizing a convex objective over the nonnegative orthant of the l1-ball and 2) minimizing a convex objective over the probability simplex.  propose an efficient and simple equality constraint elimination technique which converts the l1 and simplex constraints into order constraints.  formulate the projection onto the feasible set as an isotonic regression problem, which can be solved exactly in O(n) time via the Pool Adjacent Violators Algorithm (PAVA), where n is the dimension of the space.  design a C++ implementation of PAVA up to 25,000 times faster than scikit-learn. Our PAVA-based projection step enables the design of efficient projected subgradient methods which compare ll against projected algorithms using direct projections onto the l1-ball and onto the simplex, with projection in O(nlog(n)) exact time and O(n) expected time. Interestingly, our technique is particularly ll adapted to learning from sparse, sked, or aggregated data, by decreasing the cross-correlations beten data points.
['Jerome Thai', 'C. F. J. Wu', 'Alexey Pozdnukhov', 'Alexandre M. Bayen']
I am writing a research paper on a idea Projected sub-gradient with ℓ1 or simplex constraints via isotonic regression
[{"from": "human", "value": "I am writing a research paper on a idea Projected sub-gradient with \u21131 or simplex constraints via isotonic regression"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider two classic problems in convex optimization: 1) minimizing a convex objective over the nonnegative orthant of the l1-ball and 2) minimizing a convex objective over the probability simplex.  propose an efficient and simple equality constraint elimination technique which converts the l1 and simplex constraints into order constraints.  formulate the projection onto the feasible set as an isotonic regression problem, which can be solved exactly in O(n) time via the Pool Adjacent Violators Algorithm (PAVA), where n is the dimension of the space.  design a C++ implementation of PAVA up to 25,000 times faster than scikit-learn. Our PAVA-based projection step enables the design of efficient projected subgradient methods which compare ll against projected algorithms using direct projections onto the l1-ball and onto the simplex, with projection in O(nlog(n)) exact time and O(n) expected time. Interestingly, our technique is particularly ll adapted to learning from sparse, sked, or aggregated data, by decreasing the cross-correlations beten data points."}]