def METHOD_NAME(data):
    ''' Compute big-M values as given in Corollary 1 
        This method assumes that all scenarios are equally likely
    '''
    base = np.min([data['dMax'], data['eff'] * (data['eMax'] - data['eMin'])])
    M    = base * np.ones((data['N'], data['T'])) - data['solar']
    ell  = np.int64(np.floor(data['N'] * data['eps']) + 1)
    M   += np.sort(data['solar'], axis=0)[-ell,:]
    return M